4 Data curation and visualisation

Importing libraries

from brightway2 import *
import os               # to use "operating system dependent functionality"
import numpy as np      # "the fundamental package for scientific computing with Python"
import pandas as pd     # "high-performance, easy-to-use data structures and data analysis tools" for Python

import matplotlib.pyplot as plt
from scipy.stats import rankdata
import string
import pickle 
import seaborn as sns
import matplotlib as mpl
%matplotlib inline
from matplotlib.ticker import ScalarFormatter

Projects setting

projects.set_current('pLCA_RegAC_results_and_visualisation')

ImpactWorld+ import

# Import the specified BW2Package for the LCIA method IMPACTWorld+ file from the given path or import it to the same file location as this notebook
package_world_plus= BW2Package.import_file(r"C:\Users\kmadu\OneDrive - Danmarks Tekniske Universitet\Work\GENESIS\prospective LCA\Sensitivity and uncertainty\IMPACT-World-in-Brightway-1.28_1.46\laurepatou-IMPACT-World-in-Brightway-d949b33\Brightway_IW_damage_1_46_and_midpoint_1_28.bw2package")
# The water use method needs to be updated due to the fact that it was wrongly created.
# Filter the methods list to find the desired method for 'IMPACTWorld+ (Default_Recommended_Midpoint 1.28)'.
# Then, load the method and its characterization factors into the WS_lst variable.
IWP_mid = [m for m in methods if 'IMPACTWorld+ (Default_Recommended_Midpoint 1.28)' in m[0]]
WS = Method(IWP_mid[9])
WS_lst = WS.load()

# Define and remove certain characterization factors (CF) from the WS_lst.
ex_CF = (('biosphere3', '2404b41a-2eed-4e9d-8ab6-783946fdf5d6'), -42.95353086694035)
ex_CF_ocean = (('biosphere3', '4f0f15b3-b227-4cdc-b0b3-6412d55695d5'), 0)
WS_lst.remove(ex_CF)
WS_lst.remove(ex_CF_ocean)

# Define a new characterization factor and add it to the WS_lst.
new_CF = (('biosphere3', '8c1494a5-4987-4715-aa2d-1908c495f4eb'), 42.95353086694035)
WS_lst.append(new_CF)

# Create a new Method instance for the updated 'Water scarcity' method.
# Register the new method and write the updated characterization factors to it.
new_WS = Method(('IMPACTWorld+ (Default_Recommended_Midpoint 1.28)', 'Water scarcity'))
new_WS.register()
new_WS.write(WS_lst)

Assigning methods and units required for axis labels in visualisations

IWP_mid = [m for m in methods if 'IMPACTWorld+ (Default_Recommended_Midpoint 1.28)' in m[0]]
units_IWP_mid=['kg CO2eq.','kg CO2eq.','MJ dep.','kg dep.', 'kg NMVOCeq.','kg CFC-11eq.','CTUe','CTUh','CTUh','m3eq.','kg SO2eq.','kg SO2eq.','kg PO4eq.','kg Neq.', 'm2eq.', 'm2eq*yr.', 'kg PM2.5eq.', 'Bq C14eq.']

IWP_end = [m for m in methods if 'IMPACTWorld+ (Default_Recommended_Damage 1.46)' in m[0]]

Data Curation

Deterministic results

In this section we import the dataframes with deterministic results established in the "Calculation_of_deterministic_results" notebooks

Midpoint

opening pickles with deterministic midpoint dataframes

#Typical mission

with open(f"deterministic_midpoint_typ_2030.pkl", "rb") as f:
    var = pickle.load(f)
deterministic_midpoint_typ_2030=var

with open(f"deterministic_midpoint_typ_2040.pkl", "rb") as f:
    var = pickle.load(f)
deterministic_midpoint_typ_2040=var

with open(f"deterministic_midpoint_typ_2050.pkl", "rb") as f:
    var = pickle.load(f)
deterministic_midpoint_typ_2050=var

#Design mission

with open(f"deterministic_midpoint_des_2030.pkl", "rb") as f:
    var = pickle.load(f)
deterministic_midpoint_des_2030=var

with open(f"deterministic_midpoint_des_2040.pkl", "rb") as f:
    var = pickle.load(f)
deterministic_midpoint_des_2040=var

with open(f"deterministic_midpoint_typ_2050.pkl", "rb") as f:
    var = pickle.load(f)
deterministic_midpoint_des_2050=var

Endpoint

opening pickles with deterministic endpoint dataframes

#Typical mission

with open(f"deterministic_endpoint_typ_2030.pkl", "rb") as f:
    var = pickle.load(f)
deterministic_endpoint_typ_2030=var

with open(f"deterministic_endpoint_typ_2040.pkl", "rb") as f:
    var = pickle.load(f)
deterministic_endpoint_typ_2040=var

with open(f"deterministic_endpoint_typ_2050.pkl", "rb") as f:
    var = pickle.load(f)
deterministic_endpoint_typ_2050=var

#Design mission

with open(f"deterministic_endpoint_des_2030.pkl", "rb") as f:
    var = pickle.load(f)
deterministic_endpoint_des_2030=var

with open(f"deterministic_endpoint_des_2040.pkl", "rb") as f:
    var = pickle.load(f)
deterministic_endpoint_des_2040=var

with open(f"deterministic_endpoint_des_2050.pkl", "rb") as f:
    var = pickle.load(f)
deterministic_endpoint_des_2050=var

Life cycle stages contribution

with open(f"endpoint_stages_contribution_HH_2030.pkl", "rb") as f:
    var = pickle.load(f)
endpoint_stages_contribution_HH_2030=var
endpoint_stages_contribution_HH_2030
endpoint_stages_contribution_HH_2030['H2']=0
endpoint_stages_contribution_HH_2030['Year']='2030'


with open(f"endpoint_stages_contribution_HH_2040.pkl", "rb") as f:
    var = pickle.load(f)
endpoint_stages_contribution_HH_2040=var
endpoint_stages_contribution_HH_2040
endpoint_stages_contribution_HH_2040['Year']='2040'

with open(f"endpoint_stages_contribution_HH_2050.pkl", "rb") as f:
    var = pickle.load(f)
endpoint_stages_contribution_HH_2050=var
endpoint_stages_contribution_HH_2050
endpoint_stages_contribution_HH_2050['Year']='2050'

with open(f"endpoint_stages_contribution_EQ_2030.pkl", "rb") as f:
    var = pickle.load(f)
endpoint_stages_contribution_EQ_2030=var
endpoint_stages_contribution_EQ_2030
endpoint_stages_contribution_EQ_2030['H2']=0
endpoint_stages_contribution_EQ_2030['Year']='2030'


with open(f"endpoint_stages_contribution_EQ_2040.pkl", "rb") as f:
    var = pickle.load(f)
endpoint_stages_contribution_EQ_2040=var
endpoint_stages_contribution_EQ_2040
endpoint_stages_contribution_EQ_2040['Year']='2040'

with open(f"endpoint_stages_contribution_EQ_2050.pkl", "rb") as f:
    var = pickle.load(f)
endpoint_stages_contribution_EQ_2050=var
endpoint_stages_contribution_EQ_2050
endpoint_stages_contribution_EQ_2050['Year']='2050'

Monte Carlo Results

In this section we import the results of the Monte Carlo simulations established in the "Calculation_of_probabilistic_results" notebooks, and rearange them into dataframes

Function definitions

creating functions that rearange Monte Carlo results into dataframes

def Monte_Carlo_dataframing(names_year, df_results,i,j,a,b,level,n_iterations):

    for k in range(len(names_year)):
        name_split = str(list(names_year.keys())[k]).split('_')
        #Mission
        df_results.loc[a:b, "Mission"]=name_split[0]
        #Configuration
        df_results.loc[a:b, "Configuration"]=name_split[1]
        #Fuel
        df_results.loc[a:b, "Fuel"]=name_split[2]
        #Scenario
        df_results.loc[a:b, "Scenario"]=name_split[3]
        #Year
        df_results.loc[a:b, "Year"]=name_split[4]
        
        #Impact Level 
        #Midpoint
        if level =='midpoint':
            # 18 categories so 18*n_iteration results
            T=(18*n_iterations)-1
          #Categories and Units
            for u in range(18):
                df_results.loc[i:j, "Unit"]=units_IWP_mid[u]
                df_results.loc[i:j, "Category"]=str(IWP_mid[u][1])
                df_results.loc[i:j, "Impact Level"]='Midpoint'
                i=j+1
                j=i+(n_iterations-1)
                
        #Endpoint    
        if level=='endpoint':
            # 27 categories so 27*n_iteration results
            T=27*n_iterations-1
            #Categories
            for v in range(27):
                df_results.loc[i:j, "Category"]=str(IWP_end[v][1])
                df_results.loc[i:j, "Impact Level"]='Endpoint'

                #Units
                if df_results.loc[i:j, "Category"].isin(['Climate change, human health, short term', "Climate change, human health, long term", 'Water availability, human health', 'Photochemical oxidant formation', 'Ionizing radiation, human health', 'Ozone layer depletion', 'Human toxicity cancer, short term', 'Human toxicity cancer, long term', 'Human toxicity non-cancer, short term', 'Human toxicity non-cancer, long term', 'Particulate matter formation']).sum():
                    df_results.loc[i:j, "Unit"]='DALY'       
                else:
                    df_results.loc[i:j, "Unit"]='PDF*m2*year'
                i=j+1
                j=i+(n_iterations-1)
        
        #Values
        df_results.loc[a:b, "Value"]=np.reshape(names_year[list(names_year.keys())[k]],(T+1))
        a=b+1
        b=a+T


    return i,j,a,b
    
       

#summing function for short term damages endpoint

def Monte_Carlo_endpoint_damage_sums_ST(MC_result_aircraft_endpoint, name, n_iterations):
    endpoint= pd.DataFrame(index=[index[1] for index in IWP_end], columns=range(n_iterations))
    endpoint.loc[:]=MC_result_aircraft_endpoint

    cols_HH_ST = ["Climate change, human health, short term",'Water availability, human health','Photochemical oxidant formation','Ionizing radiation, human health','Ozone layer depletion','Human toxicity cancer, short term','Human toxicity non-cancer, short term','Particulate matter formation']
    cols_EQ_ST =['Climate change, ecosystem quality, short term','Marine acidification, short term','Land occupation, biodiversity','Water availability, freshwater ecosystem','Water availability, terrestrial ecosystem','Thermally polluted water','Freshwater ecotoxicity, short term','Freshwater acidification','Terrestrial acidification','Freshwater eutrophication','Marine eutrophication','Land transformation, biodiversity','Ionizing radiation, ecosystem quality']    #11 categories in HH
    
    # HH categories
    endpoint_sum_HH=endpoint.loc[cols_HH_ST]
    endpoint_sum_HH

    #16 categories in EQ
    endpoint_sum_EQ=endpoint.loc[cols_EQ_ST]
    endpoint_sum_EQ

    sum_EQ=endpoint_sum_EQ.sum(0)
    sum_HH=endpoint_sum_HH.sum(0)
    
    #pd.DataFrame([sum_EQ, sum_HH], columns=["EQ", "HH"])
    df = pd.DataFrame(sum_EQ, columns=["EQ"]).join(sum_HH.rename("HH")).melt(var_name="Damage", value_name="Value")
    df["Configuration"] = name
    return df


 

Pickle import

Midpoint

Typical Mission

#opening pickeled MC results for 2030 typical

with open(f"MC_typical_midpoint_2030.pkl", "rb") as f:
    var_typical_midpoint_2030 = pickle.load(f)
r_typ_aircraft_conv_kero_NDC_2030,r_typ_aircraft_conv_kero_Base_2030,r_typ_aircraft_conv_kero_PkBudg500_2030,r_typ_aircraft_GT_bat_kero_NDC_2030, r_typ_aircraft_GT_bat_kero_Base_2030,r_typ_aircraft_GT_bat_kero_PkBudg500_2030,r_typ_aircraft_conv_AAF_NDC_2030,r_typ_aircraft_conv_AAF_Base_2030,r_typ_aircraft_conv_AAF_PkBudg500_2030,r_typ_aircraft_GT_bat_AAF_NDC_2030,r_typ_aircraft_GT_bat_AAF_Base_2030,r_typ_aircraft_GT_bat_AAF_PkBudg500_2030,r_typ_aircraft_conv_ILUC_NDC_2030,r_typ_aircraft_GT_bat_ILUC_NDC_2030= var_typical_midpoint_2030

names_typ_midpoint_2030={'typical_conventional_kerosene_NDC_2030':r_typ_aircraft_conv_kero_NDC_2030,
            'typical_conventional_kerosene_Base_2030':r_typ_aircraft_conv_kero_Base_2030,
            'typical_conventional_kerosene_PkBudg500_2030':r_typ_aircraft_conv_kero_PkBudg500_2030,
            'typical_GTbat_kerosene_NDC_2030':r_typ_aircraft_GT_bat_kero_NDC_2030,
            'typical_GTbat_kerosene_Base_2030':r_typ_aircraft_GT_bat_kero_Base_2030,
            'typical_GTbat_kerosene_PkBudg500_2030':r_typ_aircraft_GT_bat_kero_PkBudg500_2030,
            'typical_conventional_AAF_NDC_2030':r_typ_aircraft_conv_AAF_NDC_2030,
            'typical_conventional_AAF_Base_2030':r_typ_aircraft_conv_AAF_Base_2030,
            'typical_conventional_AAF_PkBudg500_2030':r_typ_aircraft_conv_AAF_PkBudg500_2030,
            'typical_GTbat_AAF_NDC_2030':r_typ_aircraft_GT_bat_AAF_NDC_2030,
            'typical_GTbat_AAF_Base_2030':r_typ_aircraft_GT_bat_AAF_Base_2030,
            'typical_GTbat_AAF_PkBudg500_2030':r_typ_aircraft_GT_bat_AAF_PkBudg500_2030,
            'typical_conventional_ILUC_NDC_2030':r_typ_aircraft_conv_ILUC_NDC_2030,
            'typical_GTbat_ILUC_NDC_2030':r_typ_aircraft_GT_bat_ILUC_NDC_2030}

#opening pickeled MC results for 2040 typical

with open(f"MC_typical_midpoint_2040.pkl", "rb") as f:
    var_typical_midpoint_2040 = pickle.load(f)
r_typ_aircraft_conv_kero_NDC_2040,r_typ_aircraft_conv_kero_Base_2040,r_typ_aircraft_conv_kero_PkBudg500_2040,r_typ_aircraft_GT_bat_kero_NDC_2040,r_typ_aircraft_GT_bat_kero_Base_2040,r_typ_aircraft_GT_bat_kero_PkBudg500_2040,r_typ_aircraft_conv_AAF_NDC_2040,r_typ_aircraft_conv_AAF_Base_2040,r_typ_aircraft_conv_AAF_PkBudg500_2040,r_typ_aircraft_GT_bat_AAF_NDC_2040,r_typ_aircraft_GT_bat_AAF_Base_2040,r_typ_aircraft_GT_bat_AAF_PkBudg500_2040,r_typ_aircraft_PEMFC_bat_NDC_2040,r_typ_aircraft_PEMFC_bat_Base_2040,r_typ_aircraft_PEMFC_bat_PkBudg500_2040,r_typ_aircraft_conv_ILUC_NDC_2040,r_typ_aircraft_GT_bat_ILUC_NDC_2040= var_typical_midpoint_2040

names_typ_midpoint_2040={'typical_conventional_kerosene_NDC_2040':r_typ_aircraft_conv_kero_NDC_2040,
            'typical_conventional_kerosene_Base_2040':r_typ_aircraft_conv_kero_Base_2040,
            'typical_conventional_kerosene_PkBudg500_2040':r_typ_aircraft_conv_kero_PkBudg500_2040,
            'typical_GTbat_kerosene_NDC_2040':r_typ_aircraft_GT_bat_kero_NDC_2040,
            'typical_GTbat_kerosene_Base_2040':r_typ_aircraft_GT_bat_kero_Base_2040,
            'typical_GTbat_kerosene_PkBudg500_2040':r_typ_aircraft_GT_bat_kero_PkBudg500_2040,
            'typical_conventional_AAF_NDC_2040':r_typ_aircraft_conv_AAF_NDC_2040,
            'typical_conventional_AAF_Base_2040':r_typ_aircraft_conv_AAF_Base_2040,
            'typical_conventional_AAF_PkBudg500_2040':r_typ_aircraft_conv_AAF_PkBudg500_2040,
            'typical_GTbat_AAF_NDC_2040':r_typ_aircraft_GT_bat_AAF_NDC_2040,
            'typical_GTbat_AAF_Base_2040':r_typ_aircraft_GT_bat_AAF_Base_2040,
            'typical_GTbat_AAF_PkBudg500_2040':r_typ_aircraft_GT_bat_AAF_PkBudg500_2040,
            'typical_PEMFCbat_h2_NDC_2040':r_typ_aircraft_PEMFC_bat_NDC_2040,
            'typical_PEMFCbat_h2_Base_2040':r_typ_aircraft_PEMFC_bat_Base_2040,
            'typical_PEMFCbat_h2_PkBudg500_2040':r_typ_aircraft_PEMFC_bat_PkBudg500_2040,
            'typical_conventional_ILUC_NDC_2040':r_typ_aircraft_conv_ILUC_NDC_2040,
            'typical_GTbat_ILUC_NDC_2040':r_typ_aircraft_GT_bat_ILUC_NDC_2040}

#opening pickeled MC results for 2050 typical
with open(f"MC_typical_midpoint_2050.pkl", "rb") as f:
        var_typical_midpoint_2050 = pickle.load(f)
r_typ_aircraft_conv_kero_NDC_2050,r_typ_aircraft_conv_kero_Base_2050,r_typ_aircraft_conv_kero_PkBudg500_2050,r_typ_aircraft_conv_AAF_NDC_2050,r_typ_aircraft_conv_AAF_Base_2050,r_typ_aircraft_conv_AAF_PkBudg500_2050,r_typ_aircraft_PEMFC_bat_NDC_2050,r_typ_aircraft_PEMFC_bat_Base_2050,r_typ_aircraft_PEMFC_bat_PkBudg500_2050,r_typ_aircraft_SOFC_bat_NDC_2050,r_typ_aircraft_SOFC_bat_Base_2050,r_typ_aircraft_SOFC_bat_PkBudg500_2050,r_typ_aircraft_conv_ILUC_NDC_2050=var_typical_midpoint_2050

names_typ_midpoint_2050={'typical_conventional_kerosene_NDC_2050':r_typ_aircraft_conv_kero_NDC_2050,
            'typical_conventional_kerosene_Base_2050':r_typ_aircraft_conv_kero_Base_2050,
            'typical_conventional_kerosene_PkBudg500_2050':r_typ_aircraft_conv_kero_PkBudg500_2050,
            'typical_conventional_AAF_NDC_2050':r_typ_aircraft_conv_AAF_NDC_2050,
            'typical_conventional_AAF_Base_2050':r_typ_aircraft_conv_AAF_Base_2050,
            'typical_conventional_AAF_PkBudg500_2050':r_typ_aircraft_conv_AAF_PkBudg500_2050,
            'typical_PEMFCbat_h2_NDC_2050':r_typ_aircraft_PEMFC_bat_NDC_2050,
            'typical_PEMFCbat_h2_Base_2050':r_typ_aircraft_PEMFC_bat_Base_2050,
            'typical_PEMFCbat_h2_PkBudg500_2050':r_typ_aircraft_PEMFC_bat_PkBudg500_2050,
            'typical_SOFCbat_h2_NDC_2050':r_typ_aircraft_SOFC_bat_NDC_2050,
            'typical_SOFCbat_h2_Base_2050':r_typ_aircraft_SOFC_bat_Base_2050,
            'typical_SOFCbat_h2_PkBudg500_2050':r_typ_aircraft_SOFC_bat_PkBudg500_2050,
            'typical_conventional_ILUC_NDC_2050':r_typ_aircraft_conv_ILUC_NDC_2050,}

Design mission

#opening pickeled MC results for 2030 design

with open(f"MC_design_midpoint_2030.pkl", "rb") as f:
    var_design_midpoint_2030 = pickle.load(f)
r_des_aircraft_conv_kero_NDC_2030,r_des_aircraft_conv_kero_Base_2030,r_des_aircraft_conv_kero_PkBudg500_2030,r_des_aircraft_GT_bat_kero_NDC_2030, r_des_aircraft_GT_bat_kero_Base_2030,r_des_aircraft_GT_bat_kero_PkBudg500_2030,r_des_aircraft_conv_AAF_NDC_2030,r_des_aircraft_conv_AAF_Base_2030,r_des_aircraft_conv_AAF_PkBudg500_2030,r_des_aircraft_GT_bat_AAF_NDC_2030,r_des_aircraft_GT_bat_AAF_Base_2030,r_des_aircraft_GT_bat_AAF_PkBudg500_2030,r_des_aircraft_conv_ILUC_NDC_2030,r_des_aircraft_GT_bat_ILUC_NDC_2030= var_design_midpoint_2030

names_des_midpoint_2030={'design_conventional_kerosene_NDC_2030':r_des_aircraft_conv_kero_NDC_2030,
            'design_conventional_kerosene_Base_2030':r_des_aircraft_conv_kero_Base_2030,
            'design_conventional_kerosene_PkBudg500_2030':r_des_aircraft_conv_kero_PkBudg500_2030,
            'design_GTbat_kerosene_NDC_2030':r_des_aircraft_GT_bat_kero_NDC_2030,
            'design_GTbat_kerosene_Base_2030':r_des_aircraft_GT_bat_kero_Base_2030,
            'design_GTbat_kerosene_PkBudg500_2030':r_des_aircraft_GT_bat_kero_PkBudg500_2030,
            'design_conventional_AAF_NDC_2030':r_des_aircraft_conv_AAF_NDC_2030,
            'design_conventional_AAF_Base_2030':r_des_aircraft_conv_AAF_Base_2030,
            'design_conventional_AAF_PkBudg500_2030':r_des_aircraft_conv_AAF_PkBudg500_2030,
            'design_GTbat_AAF_NDC_2030':r_des_aircraft_GT_bat_AAF_NDC_2030,
            'design_GTbat_AAF_Base_2030':r_des_aircraft_GT_bat_AAF_Base_2030,
            'design_GTbat_AAF_PkBudg500_2030':r_des_aircraft_GT_bat_AAF_PkBudg500_2030,
            'design_conventional_ILUC_NDC_2030':r_des_aircraft_conv_ILUC_NDC_2030,
            'design_GTbat_ILUC_NDC_2030':r_des_aircraft_GT_bat_ILUC_NDC_2030}

#opening pickeled MC results for 2040 design

with open(f"MC_design_midpoint_2040.pkl", "rb") as f:
    var_design_midpoint_2040 = pickle.load(f)
r_des_aircraft_conv_kero_NDC_2040,r_des_aircraft_conv_kero_Base_2040,r_des_aircraft_conv_kero_PkBudg500_2040,r_des_aircraft_GT_bat_kero_NDC_2040,r_des_aircraft_GT_bat_kero_Base_2040,r_des_aircraft_GT_bat_kero_PkBudg500_2040,r_des_aircraft_conv_AAF_NDC_2040,r_des_aircraft_conv_AAF_Base_2040,r_des_aircraft_conv_AAF_PkBudg500_2040,r_des_aircraft_GT_bat_AAF_NDC_2040,r_des_aircraft_GT_bat_AAF_Base_2040,r_des_aircraft_GT_bat_AAF_PkBudg500_2040,r_des_aircraft_PEMFC_bat_NDC_2040,r_des_aircraft_PEMFC_bat_Base_2040,r_des_aircraft_PEMFC_bat_PkBudg500_2040,r_des_aircraft_conv_ILUC_NDC_2040,r_des_aircraft_GT_bat_ILUC_NDC_2040= var_design_midpoint_2040

names_des_midpoint_2040={'design_conventional_kerosene_NDC_2040':r_des_aircraft_conv_kero_NDC_2040,
            'design_conventional_kerosene_Base_2040':r_des_aircraft_conv_kero_Base_2040,
            'design_conventional_kerosene_PkBudg500_2040':r_des_aircraft_conv_kero_PkBudg500_2040,
            'design_GTbat_kerosene_NDC_2040':r_des_aircraft_GT_bat_kero_NDC_2040,
            'design_GTbat_kerosene_Base_2040':r_des_aircraft_GT_bat_kero_Base_2040,
            'design_GTbat_kerosene_PkBudg500_2040':r_des_aircraft_GT_bat_kero_PkBudg500_2040,
            'design_conventional_AAF_NDC_2040':r_des_aircraft_conv_AAF_NDC_2040,
            'design_conventional_AAF_Base_2040':r_des_aircraft_conv_AAF_Base_2040,
            'design_conventional_AAF_PkBudg500_2040':r_des_aircraft_conv_AAF_PkBudg500_2040,
            'design_GTbat_AAF_NDC_2040':r_des_aircraft_GT_bat_AAF_NDC_2040,
            'design_GTbat_AAF_Base_2040':r_des_aircraft_GT_bat_AAF_Base_2040,
            'design_GTbat_AAF_PkBudg500_2040':r_des_aircraft_GT_bat_AAF_PkBudg500_2040,
            'design_PEMFCbat_h2_NDC_2040':r_des_aircraft_PEMFC_bat_NDC_2040,
            'design_PEMFCbat_h2_Base_2040':r_des_aircraft_PEMFC_bat_Base_2040,
            'design_PEMFCbat_h2_PkBudg500_2040':r_des_aircraft_PEMFC_bat_PkBudg500_2040,
            'design_conventional_ILUC_NDC_2040':r_des_aircraft_conv_ILUC_NDC_2040,
            'design_GTbat_ILUC_NDC_2040':r_des_aircraft_GT_bat_ILUC_NDC_2040}

#opening pickeled MC results for 2050 design
with open(f"MC_design_midpoint_2050.pkl", "rb") as f:
        var_design_midpoint_2050 = pickle.load(f)
r_des_aircraft_conv_kero_NDC_2050,r_des_aircraft_conv_kero_Base_2050,r_des_aircraft_conv_kero_PkBudg500_2050,r_des_aircraft_conv_AAF_NDC_2050,r_des_aircraft_conv_AAF_Base_2050,r_des_aircraft_conv_AAF_PkBudg500_2050,r_des_aircraft_PEMFC_bat_NDC_2050,r_des_aircraft_PEMFC_bat_Base_2050,r_des_aircraft_PEMFC_bat_PkBudg500_2050,r_des_aircraft_SOFC_bat_NDC_2050,r_des_aircraft_SOFC_bat_Base_2050,r_des_aircraft_SOFC_bat_PkBudg500_2050,r_des_aircraft_conv_ILUC_NDC_2050=var_design_midpoint_2050

names_des_midpoint_2050={'design_conventional_kerosene_NDC_2050':r_des_aircraft_conv_kero_NDC_2050,
            'design_conventional_kerosene_Base_2050':r_des_aircraft_conv_kero_Base_2050,
            'design_conventional_kerosene_PkBudg500_2050':r_des_aircraft_conv_kero_PkBudg500_2050,
            'design_conventional_AAF_NDC_2050':r_des_aircraft_conv_AAF_NDC_2050,
            'design_conventional_AAF_Base_2050':r_des_aircraft_conv_AAF_Base_2050,
            'design_conventional_AAF_PkBudg500_2050':r_des_aircraft_conv_AAF_PkBudg500_2050,
            'design_PEMFCbat_h2_NDC_2050':r_des_aircraft_PEMFC_bat_NDC_2050,
            'design_PEMFCbat_h2_Base_2050':r_des_aircraft_PEMFC_bat_Base_2050,
            'design_PEMFCbat_h2_PkBudg500_2050':r_des_aircraft_PEMFC_bat_PkBudg500_2050,
            'design_SOFCbat_h2_NDC_2050':r_des_aircraft_SOFC_bat_NDC_2050,
            'design_SOFCbat_h2_Base_2050':r_des_aircraft_SOFC_bat_Base_2050,
            'design_SOFCbat_h2_PkBudg500_2050':r_des_aircraft_SOFC_bat_PkBudg500_2050,
            'design_conventional_ILUC_NDC_2050':r_des_aircraft_conv_ILUC_NDC_2050}

Endpoint

Typical mission

#opening pickeled MC results for 2030 typical Endpoint

with open(f"MC_typical_endpoint_2030.pkl", "rb") as f:
    var_typical_endpoint_2030 = pickle.load(f)
r_typ_aircraft_conv_kero_NDC_2030,r_typ_aircraft_conv_kero_Base_2030,r_typ_aircraft_conv_kero_PkBudg500_2030,r_typ_aircraft_GT_bat_kero_NDC_2030, r_typ_aircraft_GT_bat_kero_Base_2030,r_typ_aircraft_GT_bat_kero_PkBudg500_2030,r_typ_aircraft_conv_AAF_NDC_2030,r_typ_aircraft_conv_AAF_Base_2030,r_typ_aircraft_conv_AAF_PkBudg500_2030,r_typ_aircraft_GT_bat_AAF_NDC_2030,r_typ_aircraft_GT_bat_AAF_Base_2030,r_typ_aircraft_GT_bat_AAF_PkBudg500_2030,r_typ_aircraft_conv_ILUC_NDC_2030,r_typ_aircraft_GT_bat_ILUC_NDC_2030= var_typical_endpoint_2030

names_typ_endpoint_2030={'typical_conventional_kerosene_NDC_2030':r_typ_aircraft_conv_kero_NDC_2030,
            'typical_conventional_kerosene_Base_2030':r_typ_aircraft_conv_kero_Base_2030,
            'typical_conventional_kerosene_PkBudg500_2030':r_typ_aircraft_conv_kero_PkBudg500_2030,
            'typical_GTbat_kerosene_NDC_2030':r_typ_aircraft_GT_bat_kero_NDC_2030,
            'typical_GTbat_kerosene_Base_2030':r_typ_aircraft_GT_bat_kero_Base_2030,
            'typical_GTbat_kerosene_PkBudg500_2030':r_typ_aircraft_GT_bat_kero_PkBudg500_2030,
            'typical_conventional_AAF_NDC_2030':r_typ_aircraft_conv_AAF_NDC_2030,
            'typical_conventional_AAF_Base_2030':r_typ_aircraft_conv_AAF_Base_2030,
            'typical_conventional_AAF_PkBudg500_2030':r_typ_aircraft_conv_AAF_PkBudg500_2030,
            'typical_GTbat_AAF_NDC_2030':r_typ_aircraft_GT_bat_AAF_NDC_2030,
            'typical_GTbat_AAF_Base_2030':r_typ_aircraft_GT_bat_AAF_Base_2030,
            'typical_GTbat_AAF_PkBudg500_2030':r_typ_aircraft_GT_bat_AAF_PkBudg500_2030,
            'typical_conventional_ILUC_NDC_2030':r_typ_aircraft_conv_ILUC_NDC_2030,
            'typical_GTbat_ILUC_NDC_2030':r_typ_aircraft_GT_bat_ILUC_NDC_2030}

#opening pickeled MC results for 2040 typical

with open(f"MC_typical_endpoint_2040.pkl", "rb") as f:
    var_typical_endpoint_2040 = pickle.load(f)
r_typ_aircraft_conv_kero_NDC_2040,r_typ_aircraft_conv_kero_Base_2040,r_typ_aircraft_conv_kero_PkBudg500_2040,r_typ_aircraft_GT_bat_kero_NDC_2040,r_typ_aircraft_GT_bat_kero_Base_2040,r_typ_aircraft_GT_bat_kero_PkBudg500_2040,r_typ_aircraft_conv_AAF_NDC_2040,r_typ_aircraft_conv_AAF_Base_2040,r_typ_aircraft_conv_AAF_PkBudg500_2040,r_typ_aircraft_GT_bat_AAF_NDC_2040,r_typ_aircraft_GT_bat_AAF_Base_2040,r_typ_aircraft_GT_bat_AAF_PkBudg500_2040,r_typ_aircraft_PEMFC_bat_NDC_2040,r_typ_aircraft_PEMFC_bat_Base_2040,r_typ_aircraft_PEMFC_bat_PkBudg500_2040,r_typ_aircraft_conv_ILUC_NDC_2040,r_typ_aircraft_GT_bat_ILUC_NDC_2040= var_typical_endpoint_2040

names_typ_endpoint_2040={'typical_conventional_kerosene_NDC_2040':r_typ_aircraft_conv_kero_NDC_2040,
                'typical_conventional_kerosene_Base_2040':r_typ_aircraft_conv_kero_Base_2040,
                'typical_conventional_kerosene_PkBudg500_2040':r_typ_aircraft_conv_kero_PkBudg500_2040,
                'typical_GTbat_kerosene_NDC_2040':r_typ_aircraft_GT_bat_kero_NDC_2040,
                'typical_GTbat_kerosene_Base_2040':r_typ_aircraft_GT_bat_kero_Base_2040,
                'typical_GTbat_kerosene_PkBudg500_2040':r_typ_aircraft_GT_bat_kero_PkBudg500_2040,
                'typical_conventional_AAF_NDC_2040':r_typ_aircraft_conv_AAF_NDC_2040,
                'typical_conventional_AAF_Base_2040':r_typ_aircraft_conv_AAF_Base_2040,
                'typical_conventional_AAF_PkBudg500_2040':r_typ_aircraft_conv_AAF_PkBudg500_2040,
                'typical_GTbat_AAF_NDC_2040':r_typ_aircraft_GT_bat_AAF_NDC_2040,
                'typical_GTbat_AAF_Base_2040':r_typ_aircraft_GT_bat_AAF_Base_2040,
                'typical_GTbat_AAF_PkBudg500_2040':r_typ_aircraft_GT_bat_AAF_PkBudg500_2040,
                'typical_PEMFCbat_h2_NDC_2040':r_typ_aircraft_PEMFC_bat_NDC_2040,
                'typical_PEMFCbat_h2_Base_2040':r_typ_aircraft_PEMFC_bat_Base_2040,
                'typical_PEMFCbat_h2_PkBudg500_2040':r_typ_aircraft_PEMFC_bat_PkBudg500_2040,
                'typical_conventional_ILUC_NDC_2040':r_typ_aircraft_conv_ILUC_NDC_2040,
                'typical_GTbat_ILUC_NDC_2040':r_typ_aircraft_GT_bat_ILUC_NDC_2040}

#opening pickeled MC results for 2050 typical
with open(f"MC_typical_endpoint_2050.pkl", "rb") as f:
        var_typical_endpoint_2050 = pickle.load(f)
r_typ_aircraft_conv_kero_NDC_2050,r_typ_aircraft_conv_kero_Base_2050,r_typ_aircraft_conv_kero_PkBudg500_2050,r_typ_aircraft_conv_AAF_NDC_2050,r_typ_aircraft_conv_AAF_Base_2050,r_typ_aircraft_conv_AAF_PkBudg500_2050,r_typ_aircraft_PEMFC_bat_NDC_2050,r_typ_aircraft_PEMFC_bat_Base_2050,r_typ_aircraft_PEMFC_bat_PkBudg500_2050,r_typ_aircraft_SOFC_bat_NDC_2050,r_typ_aircraft_SOFC_bat_Base_2050,r_typ_aircraft_SOFC_bat_PkBudg500_2050,r_typ_aircraft_conv_ILUC_NDC_2050=var_typical_endpoint_2050

names_typ_endpoint_2050={'typical_conventional_kerosene_NDC_2050':r_typ_aircraft_conv_kero_NDC_2050,
            'typical_conventional_kerosene_Base_2050':r_typ_aircraft_conv_kero_Base_2050,
            'typical_conventional_kerosene_PkBudg500_2050':r_typ_aircraft_conv_kero_PkBudg500_2050,
            'typical_conventional_AAF_NDC_2050':r_typ_aircraft_conv_AAF_NDC_2050,
            'typical_conventional_AAF_Base_2050':r_typ_aircraft_conv_AAF_Base_2050,
            'typical_conventional_AAF_PkBudg500_2050':r_typ_aircraft_conv_AAF_PkBudg500_2050,
            'typical_PEMFCbat_h2_NDC_2050':r_typ_aircraft_PEMFC_bat_NDC_2050,
            'typical_PEMFCbat_h2_Base_2050':r_typ_aircraft_PEMFC_bat_Base_2050,
            'typical_PEMFCbat_h2_PkBudg500_2050':r_typ_aircraft_PEMFC_bat_PkBudg500_2050,
            'typical_SOFCbat_h2_NDC_2050':r_typ_aircraft_SOFC_bat_NDC_2050,
            'typical_SOFCbat_h2_Base_2050':r_typ_aircraft_SOFC_bat_Base_2050,
            'typical_SOFCbat_h2_PkBudg500_2050':r_typ_aircraft_SOFC_bat_PkBudg500_2050,
            'typical_conventional_ILUC_NDC_2050':r_typ_aircraft_conv_ILUC_NDC_2050}

Design mission

#opening pickeled MC results for 2030 design Endpoint

with open(f"MC_design_endpoint_2030.pkl", "rb") as f:
    var_design_endpoint_2030 = pickle.load(f)
r_des_aircraft_conv_kero_NDC_2030,r_des_aircraft_conv_kero_Base_2030,r_des_aircraft_conv_kero_PkBudg500_2030,r_des_aircraft_GT_bat_kero_NDC_2030, r_des_aircraft_GT_bat_kero_Base_2030,r_des_aircraft_GT_bat_kero_PkBudg500_2030,r_des_aircraft_conv_AAF_NDC_2030,r_des_aircraft_conv_AAF_Base_2030,r_des_aircraft_conv_AAF_PkBudg500_2030,r_des_aircraft_GT_bat_AAF_NDC_2030,r_des_aircraft_GT_bat_AAF_Base_2030,r_des_aircraft_GT_bat_AAF_PkBudg500_2030,r_des_aircraft_conv_ILUC_NDC_2030,r_des_aircraft_GT_bat_ILUC_NDC_2030= var_design_endpoint_2030

names_des_endpoint_2030={'design_conventional_kerosene_NDC_2030':r_des_aircraft_conv_kero_NDC_2030,
            'design_conventional_kerosene_Base_2030':r_des_aircraft_conv_kero_Base_2030,
            'design_conventional_kerosene_PkBudg500_2030':r_des_aircraft_conv_kero_PkBudg500_2030,
            'design_GTbat_kerosene_NDC_2030':r_des_aircraft_GT_bat_kero_NDC_2030,
            'design_GTbat_kerosene_Base_2030':r_des_aircraft_GT_bat_kero_Base_2030,
            'design_GTbat_kerosene_PkBudg500_2030':r_des_aircraft_GT_bat_kero_PkBudg500_2030,
            'design_conventional_AAF_NDC_2030':r_des_aircraft_conv_AAF_NDC_2030,
            'design_conventional_AAF_Base_2030':r_des_aircraft_conv_AAF_Base_2030,
            'design_conventional_AAF_PkBudg500_2030':r_des_aircraft_conv_AAF_PkBudg500_2030,
            'design_GTbat_AAF_NDC_2030':r_des_aircraft_GT_bat_AAF_NDC_2030,
            'design_GTbat_AAF_Base_2030':r_des_aircraft_GT_bat_AAF_Base_2030,
            'design_GTbat_AAF_PkBudg500_2030':r_des_aircraft_GT_bat_AAF_PkBudg500_2030,
            'design_conventional_ILUC_NDC_2030':r_des_aircraft_conv_ILUC_NDC_2030,
            'design_GTbat_ILUC_NDC_2030':r_des_aircraft_GT_bat_ILUC_NDC_2030}

#opening pickeled MC results for 2040 design

with open(f"MC_design_endpoint_2040.pkl", "rb") as f:
    var_design_endpoint_2040 = pickle.load(f)
r_des_aircraft_conv_kero_NDC_2040,r_des_aircraft_conv_kero_Base_2040,r_des_aircraft_conv_kero_PkBudg500_2040,r_des_aircraft_GT_bat_kero_NDC_2040,r_des_aircraft_GT_bat_kero_Base_2040,r_des_aircraft_GT_bat_kero_PkBudg500_2040,r_des_aircraft_conv_AAF_NDC_2040,r_des_aircraft_conv_AAF_Base_2040,r_des_aircraft_conv_AAF_PkBudg500_2040,r_des_aircraft_GT_bat_AAF_NDC_2040,r_des_aircraft_GT_bat_AAF_Base_2040,r_des_aircraft_GT_bat_AAF_PkBudg500_2040,r_des_aircraft_PEMFC_bat_NDC_2040,r_des_aircraft_PEMFC_bat_Base_2040,r_des_aircraft_PEMFC_bat_PkBudg500_2040,r_des_aircraft_conv_ILUC_NDC_2040,r_des_aircraft_GT_bat_ILUC_NDC_2040= var_design_endpoint_2040

names_des_endpoint_2040={'design_conventional_kerosene_NDC_2040':r_des_aircraft_conv_kero_NDC_2040,
                'design_conventional_kerosene_Base_2040':r_des_aircraft_conv_kero_Base_2040,
                'design_conventional_kerosene_PkBudg500_2040':r_des_aircraft_conv_kero_PkBudg500_2040,
                'design_GTbat_kerosene_NDC_2040':r_des_aircraft_GT_bat_kero_NDC_2040,
                'design_GTbat_kerosene_Base_2040':r_des_aircraft_GT_bat_kero_Base_2040,
                'design_GTbat_kerosene_PkBudg500_2040':r_des_aircraft_GT_bat_kero_PkBudg500_2040,
                'design_conventional_AAF_NDC_2040':r_des_aircraft_conv_AAF_NDC_2040,
                'design_conventional_AAF_Base_2040':r_des_aircraft_conv_AAF_Base_2040,
                'design_conventional_AAF_PkBudg500_2040':r_des_aircraft_conv_AAF_PkBudg500_2040,
                'design_GTbat_AAF_NDC_2040':r_des_aircraft_GT_bat_AAF_NDC_2040,
                'design_GTbat_AAF_Base_2040':r_des_aircraft_GT_bat_AAF_Base_2040,
                'design_GTbat_AAF_PkBudg500_2040':r_des_aircraft_GT_bat_AAF_PkBudg500_2040,
                'design_PEMFCbat_h2_NDC_2040':r_des_aircraft_PEMFC_bat_NDC_2040,
                'design_PEMFCbat_h2_Base_2040':r_des_aircraft_PEMFC_bat_Base_2040,
                'design_PEMFCbat_h2_PkBudg500_2040':r_des_aircraft_PEMFC_bat_PkBudg500_2040,
                'design_conventional_ILUC_NDC_2040':r_des_aircraft_conv_ILUC_NDC_2040,
                'design_GTbat_ILUC_NDC_2040':r_des_aircraft_GT_bat_ILUC_NDC_2040}
                        

#opening pickeled MC results for 2050 design
with open(f"MC_design_endpoint_2050.pkl", "rb") as f:
        var_design_endpoint_2050 = pickle.load(f)
r_des_aircraft_conv_kero_NDC_2050,r_des_aircraft_conv_kero_Base_2050,r_des_aircraft_conv_kero_PkBudg500_2050,r_des_aircraft_conv_AAF_NDC_2050,r_des_aircraft_conv_AAF_Base_2050,r_des_aircraft_conv_AAF_PkBudg500_2050,r_des_aircraft_PEMFC_bat_NDC_2050,r_des_aircraft_PEMFC_bat_Base_2050,r_des_aircraft_PEMFC_bat_PkBudg500_2050,r_des_aircraft_SOFC_bat_NDC_2050,r_des_aircraft_SOFC_bat_Base_2050,r_des_aircraft_SOFC_bat_PkBudg500_2050,r_des_aircraft_conv_ILUC_NDC_2050=var_design_endpoint_2050

names_des_endpoint_2050={'design_conventional_kerosene_NDC_2050':r_des_aircraft_conv_kero_NDC_2050,
            'design_conventional_kerosene_Base_2050':r_des_aircraft_conv_kero_Base_2050,
            'design_conventional_kerosene_PkBudg500_2050':r_des_aircraft_conv_kero_PkBudg500_2050,
            'design_conventional_AAF_NDC_2050':r_des_aircraft_conv_AAF_NDC_2050,
            'design_conventional_AAF_Base_2050':r_des_aircraft_conv_AAF_Base_2050,
            'design_conventional_AAF_PkBudg500_2050':r_des_aircraft_conv_AAF_PkBudg500_2050,
            'design_PEMFCbat_h2_NDC_2050':r_des_aircraft_PEMFC_bat_NDC_2050,
            'design_PEMFCbat_h2_Base_2050':r_des_aircraft_PEMFC_bat_Base_2050,
            'design_PEMFCbat_h2_PkBudg500_2050':r_des_aircraft_PEMFC_bat_PkBudg500_2050,
            'design_SOFCbat_h2_NDC_2050':r_des_aircraft_SOFC_bat_NDC_2050,
            'design_SOFCbat_h2_Base_2050':r_des_aircraft_SOFC_bat_Base_2050,
            'design_SOFCbat_h2_PkBudg500_2050':r_des_aircraft_SOFC_bat_PkBudg500_2050,
            'design_conventional_ILUC_NDC_2050':r_des_aircraft_conv_ILUC_NDC_2050}

Dataframes

Midpoint

#initialising empty DF

n_config=+len(var_typical_midpoint_2030)+len(var_typical_midpoint_2040)+len(var_typical_midpoint_2050)+len(var_design_midpoint_2030)+len(var_design_midpoint_2040)+len(var_design_midpoint_2050)
MC_results_Midpoint= pd.DataFrame(index=range(18*500*n_config),columns=['Year','Mission','Configuration','Fuel','Scenario','Impact Level', 'Category','Value', 'Unit'])

# using the function defined above to provide a dataframe with midpoint results

i,j,a,b= Monte_Carlo_dataframing(names_typ_midpoint_2030,MC_results_Midpoint,0,499,0,18*500-1,'midpoint',500)
i,j,a,b= Monte_Carlo_dataframing(names_typ_midpoint_2040,MC_results_Midpoint,i,j,a,b,'midpoint',500)
i,j,a,b= Monte_Carlo_dataframing(names_typ_midpoint_2050,MC_results_Midpoint,i,j,a,b,'midpoint',500)
i,j,a,b= Monte_Carlo_dataframing(names_des_midpoint_2030,MC_results_Midpoint,i,j,a,b,'midpoint',500)
i,j,a,b= Monte_Carlo_dataframing(names_des_midpoint_2040,MC_results_Midpoint,i,j,a,b,'midpoint',500)
i,j,a,b= Monte_Carlo_dataframing(names_des_midpoint_2050,MC_results_Midpoint,i,j,a,b,'midpoint',500)

MC_results_Midpoint=MC_results_Midpoint.replace(['typical'],'Typical')
MC_results_Midpoint=MC_results_Midpoint.replace(['design'],'Design')
MC_results_Midpoint=MC_results_Midpoint.replace(['typical'],'Typical')
MC_results_Midpoint=MC_results_Midpoint.replace(['conventional'],'Conventional')
MC_results_Midpoint=MC_results_Midpoint.replace(['GTbat'],'GT-bat')
MC_results_Midpoint=MC_results_Midpoint.replace(['PEMFCbat'],'PEMFC-bat')
MC_results_Midpoint=MC_results_Midpoint.replace(['SOFCbat'],'SOFC-bat')
MC_results_Midpoint=MC_results_Midpoint.replace(['kerosene'],'Kerosene')
MC_results_Midpoint=MC_results_Midpoint.replace(['midpoint'],'Midpoint')
MC_results_Midpoint=MC_results_Midpoint.replace(['h2'],'H2')
MC_results_Midpoint

	Year	Mission	Configuration	Fuel	Scenario	Impact Level	Category	Value	Unit
0	2030	Typical	Conventional	Kerosene	NDC	Midpoint	Climate change, short term	1958.098319	kg CO2eq.
1	2030	Typical	Conventional	Kerosene	NDC	Midpoint	Climate change, short term	1862.055026	kg CO2eq.
2	2030	Typical	Conventional	Kerosene	NDC	Midpoint	Climate change, short term	1788.645990	kg CO2eq.
3	2030	Typical	Conventional	Kerosene	NDC	Midpoint	Climate change, short term	1880.183990	kg CO2eq.
4	2030	Typical	Conventional	Kerosene	NDC	Midpoint	Climate change, short term	1781.427353	kg CO2eq.
...	...	...	...	...	...	...	...	...	...
791995	2050	Design	Conventional	ILUC	NDC	Midpoint	Ionizing radiations	7489.063927	Bq C14eq.
791996	2050	Design	Conventional	ILUC	NDC	Midpoint	Ionizing radiations	7541.058077	Bq C14eq.
791997	2050	Design	Conventional	ILUC	NDC	Midpoint	Ionizing radiations	8196.452982	Bq C14eq.
791998	2050	Design	Conventional	ILUC	NDC	Midpoint	Ionizing radiations	10695.624471	Bq C14eq.
791999	2050	Design	Conventional	ILUC	NDC	Midpoint	Ionizing radiations	7429.042347	Bq C14eq.

792000 rows × 9 columns

Endpoint

Damage level

#initialising empty DF

n_config=len(var_typical_endpoint_2030)+len(var_typical_endpoint_2040)+len(var_typical_endpoint_2050)+len(var_design_endpoint_2030)+len(var_design_endpoint_2040)+len(var_design_endpoint_2050)
MC_results_Endpoint= pd.DataFrame(index=range(27*500*n_config),columns=['Year','Mission','Configuration','Fuel','Scenario','Impact Level', 'Category','Value', 'Unit'])

# using the function defined above to provide a dataframe with endpoint results

i,j,a,b= Monte_Carlo_dataframing(names_typ_endpoint_2030,MC_results_Endpoint,0,499,0,27*500-1,'endpoint',500)
i,j,a,b= Monte_Carlo_dataframing(names_typ_endpoint_2040,MC_results_Endpoint,i,j,a,b,'endpoint',500)
i,j,a,b= Monte_Carlo_dataframing(names_typ_endpoint_2050,MC_results_Endpoint,i,j,a,b,'endpoint',500)
i,j,a,b= Monte_Carlo_dataframing(names_des_endpoint_2030,MC_results_Endpoint,i,j,a,b,'endpoint',500)
i,j,a,b= Monte_Carlo_dataframing(names_des_endpoint_2040,MC_results_Endpoint,i,j,a,b,'endpoint',500)
i,j,a,b= Monte_Carlo_dataframing(names_des_endpoint_2050,MC_results_Endpoint,i,j,a,b,'endpoint',500)

MC_results_Endpoint=MC_results_Endpoint.replace(['typical'],'Typical')
MC_results_Endpoint=MC_results_Endpoint.replace(['design'],'Design')
MC_results_Endpoint=MC_results_Endpoint.replace(['typical'],'Typical')
MC_results_Endpoint=MC_results_Endpoint.replace(['conventional'],'Conventional')
MC_results_Endpoint=MC_results_Endpoint.replace(['GTbat'],'GT-bat')
MC_results_Endpoint=MC_results_Endpoint.replace(['PEMFCbat'],'PEMFC-bat')
MC_results_Endpoint=MC_results_Endpoint.replace(['SOFCbat'],'SOFC-bat')
MC_results_Endpoint=MC_results_Endpoint.replace(['kerosene'],'Kerosene')
MC_results_Endpoint=MC_results_Endpoint.replace(['h2'],'H2')
MC_results_Endpoint=MC_results_Endpoint.replace(['endpoint'],'Endpoint')
MC_results_Endpoint

	Year	Mission	Configuration	Fuel	Scenario	Impact Level	Category	Value	Unit
0	2030	Typical	Conventional	Kerosene	NDC	Endpoint	Climate change, ecosystem quality, short term	3.465578e+02	PDF*m2*year
1	2030	Typical	Conventional	Kerosene	NDC	Endpoint	Climate change, ecosystem quality, short term	3.295668e+02	PDF*m2*year
2	2030	Typical	Conventional	Kerosene	NDC	Endpoint	Climate change, ecosystem quality, short term	3.165707e+02	PDF*m2*year
3	2030	Typical	Conventional	Kerosene	NDC	Endpoint	Climate change, ecosystem quality, short term	3.327703e+02	PDF*m2*year
4	2030	Typical	Conventional	Kerosene	NDC	Endpoint	Climate change, ecosystem quality, short term	3.152945e+02	PDF*m2*year
...	...	...	...	...	...	...	...	...	...
1187995	2050	Design	Conventional	ILUC	NDC	Endpoint	Ionizing radiation, ecosystem quality	2.913296e-07	PDF*m2*year
1187996	2050	Design	Conventional	ILUC	NDC	Endpoint	Ionizing radiation, ecosystem quality	3.276337e-07	PDF*m2*year
1187997	2050	Design	Conventional	ILUC	NDC	Endpoint	Ionizing radiation, ecosystem quality	2.838208e-07	PDF*m2*year
1187998	2050	Design	Conventional	ILUC	NDC	Endpoint	Ionizing radiation, ecosystem quality	3.698479e-07	PDF*m2*year
1187999	2050	Design	Conventional	ILUC	NDC	Endpoint	Ionizing radiation, ecosystem quality	2.875093e-07	PDF*m2*year

1188000 rows × 9 columns

Aggregated damages

For the climate change, human toxicity, marine acidification and freshwater ecotoxicicty damages, the impacts are only considered over the short-term time span (from t=0 to t=100 years)
See world impact+ article from Bulle et al. (2019)

# using the function defined above to provide a dataframe with endpoint results that are aggregated

dfs = []
for k, v in names_typ_endpoint_2030.items():
    dfs.append(Monte_Carlo_endpoint_damage_sums_ST(v, k,500))
for k, v in names_typ_endpoint_2040.items():
    dfs.append(Monte_Carlo_endpoint_damage_sums_ST(v, k,500))
for k, v in names_typ_endpoint_2050.items():
    dfs.append(Monte_Carlo_endpoint_damage_sums_ST(v, k,500))
for k, v in names_des_endpoint_2030.items():
    dfs.append(Monte_Carlo_endpoint_damage_sums_ST(v, k,500))
for k, v in names_des_endpoint_2040.items():
    dfs.append(Monte_Carlo_endpoint_damage_sums_ST(v, k,500))
for k, v in names_des_endpoint_2050.items():
    dfs.append(Monte_Carlo_endpoint_damage_sums_ST(v, k,500))

endpoints_ST = pd.concat(dfs)    
endpoints_ST[["Mission", "Configuration", "Fuel", "Scenario", "Year"]] = endpoints_ST["Configuration"].str.split("_", expand=True)
endpoints_ST['Unit'] = ''
endpoints_ST['Impact Level'] = 'endpoint'
endpoints_ST[['Year', 'Mission', 'Configuration','Fuel','Scenario','Impact Level','Damage','Value','Unit']]
if  endpoints_ST.loc[:,'Damage'].isin(['EQ']).sum():
    endpoints_ST['Unit'] = 'PDF*m2*year'
else:
    endpoints_ST['Unit'] = 'DALY'
    
    
endpoints_ST=endpoints_ST.replace(['typical'],'Typical')
endpoints_ST=endpoints_ST.replace(['design'],'Design')
endpoints_ST=endpoints_ST.replace(['typical'],'Typical')
endpoints_ST=endpoints_ST.replace(['conventional'],'Conventional')
endpoints_ST=endpoints_ST.replace(['GTbat'],'GT-bat')
endpoints_ST=endpoints_ST.replace(['PEMFCbat'],'PEMFC-bat')
endpoints_ST=endpoints_ST.replace(['SOFCbat'],'SOFC-bat')
endpoints_ST=endpoints_ST.replace(['h2'],'H2')
endpoints_ST=endpoints_ST.replace(['kerosene'],'Kerosene')
MC_endpoints_ST=endpoints_ST.replace(['Endpoint'],'Endpoint')
MC_endpoints_ST

	Damage	Value	Configuration	Mission	Fuel	Scenario	Year	Unit	Impact Level
0	EQ	666.237944	Conventional	Typical	Kerosene	NDC	2030	PDF*m2*year	endpoint
1	EQ	610.300511	Conventional	Typical	Kerosene	NDC	2030	PDF*m2*year	endpoint
2	EQ	604.902153	Conventional	Typical	Kerosene	NDC	2030	PDF*m2*year	endpoint
3	EQ	629.974808	Conventional	Typical	Kerosene	NDC	2030	PDF*m2*year	endpoint
4	EQ	597.209666	Conventional	Typical	Kerosene	NDC	2030	PDF*m2*year	endpoint
...	...	...	...	...	...	...	...	...	...
995	HH	0.008415	Conventional	Design	ILUC	NDC	2050	PDF*m2*year	endpoint
996	HH	0.007955	Conventional	Design	ILUC	NDC	2050	PDF*m2*year	endpoint
997	HH	0.007579	Conventional	Design	ILUC	NDC	2050	PDF*m2*year	endpoint
998	HH	0.007801	Conventional	Design	ILUC	NDC	2050	PDF*m2*year	endpoint
999	HH	0.007819	Conventional	Design	ILUC	NDC	2050	PDF*m2*year	endpoint

88000 rows × 9 columns

Characterized results

In this section we calculate the characterized results based on medians from the Monte Carlo simulations

Medians

#medians of monte carlo endpoint results for each impact category and configuration present in the MC_results_Endpoint dataframe

n_iterations=500
length=len(MC_results_Endpoint)/n_iterations
Medians_endpoint=pd.DataFrame(index=range(int(length)),columns=['Year', 'Mission','Configuration', 'Fuel','Scenario', 'Impact Level','Category','Value', 'Unit'])


for k in range(int(length)):
    Medians_endpoint.iloc[[k]]= MC_results_Endpoint.iloc[[(n_iterations)*k]] #modulo 500
    Medians_endpoint.loc[k,'Value']=MC_results_Endpoint.loc[n_iterations*k:n_iterations*k+500,'Value'].median()
    
Medians_endpoint_all_results=Medians_endpoint
Medians_endpoint_all_results['Summary']=Medians_endpoint_all_results['Year']+" "+Medians_endpoint_all_results['Configuration']+' ('+Medians_endpoint_all_results['Fuel']+')'
Medians_endpoint_all_results['Category and unit']=Medians_endpoint_all_results['Category']+' ['+Medians_endpoint_all_results['Unit']+']'

#medians of monte carlo midpoint results for each impact category and configuration present in the MC_results_Midpoint dataframe

n_iterations=500
length=len(MC_results_Midpoint)/n_iterations
Medians_midpoint=pd.DataFrame(index=range(int(length)),columns=['Year', 'Mission','Configuration', 'Fuel','Scenario', 'Impact Level','Category','Value', 'Unit'])


for k in range(int(length)):
    Medians_midpoint.iloc[[k]]= MC_results_Midpoint.iloc[[(n_iterations)*k]] #modulo 500
    Medians_midpoint.loc[k,'Value']=MC_results_Midpoint.loc[n_iterations*k:n_iterations*k+500,'Value'].median()
    
Medians_midpoint['Category and unit']=Medians_midpoint['Category']+' ['+Medians_midpoint['Unit']+']'

Endpoint dataframe

In this section we present dataframes with endpoint characterized results for each SSP scenario and mission type

NDC

Medians_endpoint_all_results_NDC=Medians_endpoint_all_results[Medians_endpoint_all_results['Scenario']=='NDC']

Typical Mission

NDC_typical_endpoint_typical=Medians_endpoint_all_results_NDC[Medians_endpoint_all_results_NDC['Mission']=='Typical']
NDC_typical_endpoint_typical=NDC_typical_endpoint_typical.pivot('Category and unit','Summary','Value')
NDC_typical_endpoint_typical

C:\Users\kmadu\AppData\Local\Temp\ipykernel_17960\3670571744.py:2: FutureWarning: In a future version of pandas all arguments of DataFrame.pivot will be keyword-only.
  NDC_typical_endpoint_typical=NDC_typical_endpoint_typical.pivot('Category and unit','Summary','Value')

Summary	2030 Conventional (AAF)	2030 Conventional (ILUC)	2030 Conventional (Kerosene)	2030 GT-bat (AAF)	2030 GT-bat (ILUC)	2030 GT-bat (Kerosene)	2040 Conventional (AAF)	2040 Conventional (ILUC)	2040 Conventional (Kerosene)	2040 GT-bat (AAF)	2040 GT-bat (ILUC)	2040 GT-bat (Kerosene)	2040 PEMFC-bat (H2)	2050 Conventional (AAF)	2050 Conventional (ILUC)	2050 Conventional (Kerosene)	2050 PEMFC-bat (H2)	2050 SOFC-bat (H2)
Category and unit
Climate change, ecosystem quality, long term [PDF*m2*year]	373.593966	548.62447	1174.009091	408.819477	547.724501	984.78505	310.308337	336.048075	1062.328517	252.431841	458.771045	673.893192	213.186644	247.199803	358.287081	1018.615449	168.612649	156.351888
Climate change, ecosystem quality, short term [PDF*m2*year]	172.316605	221.094746	341.681672	170.811855	209.4879	289.036318	144.762142	130.734214	310.46084	107.003706	186.586035	200.029706	70.451015	115.449576	146.708791	296.826737	57.114787	53.614942
Climate change, human health, long term [DALY]	0.0017	0.002494	0.005347	0.001861	0.002491	0.004484	0.001412	0.001533	0.004839	0.001147	0.002086	0.003068	0.000971	0.001127	0.001629	0.004639	0.000768	0.000712
Climate change, human health, short term [DALY]	0.000798	0.001023	0.001579	0.00079	0.000969	0.001336	0.00067	0.000605	0.001435	0.000495	0.000863	0.000925	0.000326	0.000534	0.000679	0.001372	0.000264	0.000248
Freshwater acidification [PDF*m2*year]	15.007743	15.023673	12.010509	12.838089	12.826743	9.872749	13.534173	9.792578	11.266306	9.824503	13.570244	8.537776	8.113268	12.094345	12.100958	10.889082	6.773565	6.010614
Freshwater ecotoxicity, long term [PDF*m2*year]	23392.242086	23392.242086	20726.729491	28967.173006	28967.173006	24431.852901	24550.584296	28365.982862	21292.801748	28365.982855	24550.584296	26602.010864	44196.854806	26504.942118	26504.942118	21572.867159	43609.551137	39531.603724
Freshwater ecotoxicity, short term [PDF*m2*year]	24.640533	24.640533	34.918195	28.529681	28.529681	34.067384	24.922241	24.09991	34.503722	24.09991	24.922241	29.544248	29.746197	24.275411	24.275411	33.873563	29.010504	29.025955
Freshwater eutrophication [PDF*m2*year]	0.911352	0.911352	0.773626	0.787676	0.787676	0.65816	0.777002	0.491783	0.703301	0.491783	0.777002	0.446803	0.391708	0.626957	0.626957	0.679656	0.364327	0.292507
Human toxicity cancer, long term [DALY]	0.000007	0.000007	0.000008	0.000009	0.000009	0.000009	0.000008	0.000009	0.000008	0.000009	0.000008	0.000009	0.000015	0.000008	0.000008	0.000008	0.000015	0.000013
Human toxicity cancer, short term [DALY]	0.0002	0.0002	0.000147	0.000313	0.000313	0.00022	0.00022	0.000277	0.000153	0.000277	0.00022	0.00024	0.000539	0.000267	0.000267	0.000151	0.000512	0.000447
Human toxicity non-cancer, long term [DALY]	0.00026	0.00026	0.000276	0.000323	0.000323	0.000307	0.000272	0.000313	0.000276	0.000313	0.000272	0.000315	0.000499	0.000296	0.000296	0.000275	0.000497	0.000446
Human toxicity non-cancer, short term [DALY]	0.000341	0.000341	0.000302	0.000427	0.000427	0.000368	0.000345	0.000396	0.000301	0.000396	0.000345	0.000372	0.000565	0.000358	0.000358	0.000299	0.000557	0.000505
Ionizing radiation, ecosystem quality [PDF*m2*year]	0.0	0.0	0.0	0.000001	0.000001	0.000001	0.0	0.0	0.0	0.0	0.0	0.0	0.000001	0.0	0.0	0.0	0.0	0.0
Ionizing radiation, human health [DALY]	0.000002	0.000002	0.000003	0.000008	0.000008	0.000006	0.000002	0.000003	0.000003	0.000003	0.000002	0.000004	0.00001	0.000002	0.000002	0.000002	0.000005	0.000004
Land occupation, biodiversity [PDF*m2*year]	653.309082	653.309082	22.725733	532.202053	532.202053	27.956021	583.832807	340.879258	22.981712	340.879258	583.832807	27.599897	46.656081	534.958387	534.958387	22.796804	40.481212	36.764671
Land transformation, biodiversity [PDF*m2*year]	72.602009	72.602009	114.952158	99.402707	99.402707	116.29628	74.623333	83.615669	110.726471	83.615669	74.623333	103.978032	152.182589	79.982763	79.982763	107.056593	142.275864	123.639343
Marine acidification, long term [PDF*m2*year]	76.179902	118.306849	283.245637	86.694194	120.249726	237.148274	62.752183	73.929288	256.255612	53.517562	98.678711	161.793198	48.875865	49.834044	76.597096	245.628552	38.246939	35.646648
Marine acidification, short term [PDF*m2*year]	8.269529	12.84252	30.750577	9.410883	13.053425	25.743557	6.811915	8.025219	27.818861	5.809472	10.711834	17.571089	5.305604	5.409617	8.314817	26.666939	4.151806	3.869538
Marine eutrophication [PDF*m2*year]	3.602866	3.587381	1.912251	2.882054	2.878442	1.453253	3.293062	1.939584	1.850725	1.944841	3.288865	1.149948	1.095288	2.968799	2.957397	1.802553	1.012422	0.929792
Ozone layer depletion [DALY]	0.0	0.0	0.000001	0.0	0.0	0.000001	0.0	0.0	0.000001	0.0	0.0	0.0	0.0	0.0	0.0	0.000001	0.0	0.0
Particulate matter formation [DALY]	0.000742	0.00074	0.000441	0.000701	0.000701	0.000428	0.000655	0.000509	0.000408	0.00051	0.000656	0.000373	0.00045	0.000577	0.000577	0.000388	0.000372	0.000339
Photochemical oxidant formation [DALY]	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Terrestrial acidification [PDF*m2*year]	124.313835	124.408015	83.389207	103.253388	103.151885	66.76577	111.038651	75.034556	78.275704	75.100951	111.409189	56.553151	52.167279	97.485832	97.543573	75.652575	43.432269	38.576516
Thermally polluted water [PDF*m2*year]	0.001546	0.001546	0.001147	0.004256	0.004256	0.002587	0.001453	0.001992	0.000935	0.001992	0.001453	0.001712	0.004607	0.001215	0.001215	0.0007	0.002252	0.001912
Water availability, freshwater ecosystem [PDF*m2*year]	0.016753	0.016753	0.006341	0.028734	0.028734	0.013715	0.014795	0.013965	0.005218	0.013965	0.014795	0.008649	0.022501	0.012465	0.012465	0.004188	0.0129	0.010962
Water availability, human health [DALY]	0.00276	0.00276	0.00039	0.002623	0.002623	0.000594	0.002432	0.001695	0.000388	0.001695	0.002432	0.000561	0.001497	0.002087	0.002087	0.000362	0.00132	0.001122
Water availability, terrestrial ecosystem [PDF*m2*year]	0.405326	0.405326	0.0421	0.338226	0.338226	0.047696	0.352991	0.21803	0.040753	0.21803	0.352991	0.044253	0.08831	0.295411	0.295411	0.03902	0.083035	0.072341

Design Mission

Medians_endpoint_all_results_NDC_design=Medians_endpoint_all_results_NDC[Medians_endpoint_all_results_NDC['Mission']=='Design']
Medians_endpoint_all_results_NDC_design=Medians_endpoint_all_results_NDC[Medians_endpoint_all_results_NDC['Mission']=='Design']
NDC_design=Medians_endpoint_all_results_NDC_design.pivot('Category and unit','Summary','Value')
NDC_design

C:\Users\kmadu\AppData\Local\Temp\ipykernel_17960\616642206.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.pivot will be keyword-only.
  NDC_design=Medians_endpoint_all_results_NDC_design.pivot('Category and unit','Summary','Value')

Summary	2030 Conventional (AAF)	2030 Conventional (ILUC)	2030 Conventional (Kerosene)	2030 GT-bat (AAF)	2030 GT-bat (ILUC)	2030 GT-bat (Kerosene)	2040 Conventional (AAF)	2040 Conventional (ILUC)	2040 Conventional (Kerosene)	2040 GT-bat (AAF)	2040 GT-bat (ILUC)	2040 GT-bat (Kerosene)	2040 PEMFC-bat (H2)	2050 Conventional (AAF)	2050 Conventional (ILUC)	2050 Conventional (Kerosene)	2050 PEMFC-bat (H2)	2050 SOFC-bat (H2)
Category and unit
Climate change, ecosystem quality, long term [PDF*m2*year]	719.650172	1137.124501	2578.743868	925.007372	1340.106811	2579.33737	576.810615	756.308235	2334.602141	496.941487	930.726158	1845.290397	446.838227	464.036402	730.910613	2251.387255	341.275251	303.103199
Climate change, ecosystem quality, short term [PDF*m2*year]	348.913466	465.932038	745.728483	410.329284	525.092447	746.639345	286.685467	307.1028	674.625546	232.529922	386.552637	535.379125	142.514019	228.34825	304.243106	650.239522	111.248889	99.024884
Climate change, human health, long term [DALY]	0.003287	0.00517	0.011743	0.004205	0.006094	0.011749	0.002619	0.003443	0.010638	0.002258	0.004233	0.008403	0.002036	0.002109	0.003325	0.010253	0.001556	0.001381
Climate change, human health, short term [DALY]	0.001615	0.002156	0.003446	0.001899	0.002429	0.003451	0.001327	0.001421	0.003118	0.001076	0.001789	0.002474	0.000659	0.001057	0.001408	0.003005	0.000514	0.000458
Freshwater acidification [PDF*m2*year]	31.316987	31.346993	24.349665	35.022531	35.070342	25.479944	28.021624	24.088206	22.743503	24.111492	28.00446	22.054776	17.252913	24.900926	24.843731	21.955887	14.603733	12.316105
Freshwater ecotoxicity, long term [PDF*m2*year]	27770.419454	27770.419454	21620.011178	43946.652404	43946.652416	25983.711804	29369.314804	32560.397493	21776.525157	32560.397492	29369.314804	38678.667256	99377.788079	33661.434172	33661.434172	22145.411197	93179.139107	80524.108868
Freshwater ecotoxicity, short term [PDF*m2*year]	37.459936	37.459936	60.731073	51.209919	51.209919	64.183669	35.555449	33.250681	57.754107	33.250681	35.555449	57.401215	65.358738	34.62463	34.62463	56.293646	59.666662	54.30195
Freshwater eutrophication [PDF*m2*year]	2.088919	2.088919	1.75648	2.180668	2.180668	1.78183	1.771771	1.366787	1.609746	1.366787	1.771771	1.268911	1.265435	1.427834	1.427834	1.554971	1.129233	0.89292
Human toxicity cancer, long term [DALY]	0.000008	0.000008	0.00001	0.000013	0.000013	0.000011	0.000008	0.00001	0.00001	0.00001	0.000008	0.000015	0.000034	0.00001	0.00001	0.00001	0.000032	0.000027
Human toxicity cancer, short term [DALY]	0.000311	0.000311	0.00019	0.00064	0.00064	0.000276	0.000347	0.000388	0.00019	0.000388	0.000347	0.000532	0.001616	0.000458	0.000458	0.000189	0.001514	0.001287
Human toxicity non-cancer, long term [DALY]	0.000276	0.000276	0.000313	0.00045	0.00045	0.000352	0.000299	0.000336	0.000308	0.000336	0.000299	0.000473	0.001111	0.000357	0.000357	0.000307	0.001039	0.000882
Human toxicity non-cancer, short term [DALY]	0.000422	0.000422	0.000332	0.000644	0.000644	0.00042	0.000426	0.000466	0.000326	0.000466	0.000426	0.000525	0.001207	0.000459	0.000459	0.000323	0.001123	0.000969
Ionizing radiation, ecosystem quality [PDF*m2*year]	0.0	0.0	0.000001	0.000001	0.000001	0.000001	0.0	0.0	0.000001	0.0	0.0	0.000001	0.000002	0.0	0.0	0.000001	0.000001	0.000001
Ionizing radiation, human health [DALY]	0.000003	0.000003	0.000006	0.00002	0.00002	0.000009	0.000003	0.000005	0.000006	0.000005	0.000003	0.000014	0.000037	0.000003	0.000003	0.000005	0.000016	0.000013
Land occupation, biodiversity [PDF*m2*year]	1492.994781	1492.994781	26.869642	1507.097555	1507.097555	32.896824	1330.220443	995.35453	26.693175	995.35453	1330.220443	50.526015	120.122205	1217.126019	1217.126019	26.633454	94.144288	81.434546
Land transformation, biodiversity [PDF*m2*year]	118.57439	118.57439	216.590384	210.897996	210.897996	231.395515	118.629639	122.22682	202.561398	122.22682	118.629639	264.879694	454.836362	132.943594	132.943594	196.100335	409.998796	347.827726
Marine acidification, long term [PDF*m2*year]	142.586105	242.893247	622.861042	189.183028	289.429015	622.669337	111.806333	161.978007	564.072362	98.961733	197.149036	443.766966	99.878732	90.264004	153.945142	543.361076	75.099955	66.709845
Marine acidification, short term [PDF*m2*year]	15.478097	26.366702	67.640587	20.536316	31.418282	67.615251	12.136872	17.583139	61.269863	10.742557	21.401047	48.183907	10.842099	9.798395	16.711151	58.999793	8.152298	7.241529
Marine eutrophication [PDF*m2*year]	8.220768	8.219903	4.341855	9.081705	9.111746	4.808427	7.492667	6.162307	4.170882	6.159837	7.466423	4.03558	2.683875	6.743322	6.736285	4.057601	2.272041	1.999703
Ozone layer depletion [DALY]	0.0	0.0	0.000002	0.0	0.0	0.000002	0.0	0.0	0.000002	0.0	0.0	0.000001	0.0	0.0	0.0	0.000002	0.0	0.0
Particulate matter formation [DALY]	0.001481	0.001482	0.000788	0.001714	0.001714	0.000855	0.001291	0.001096	0.000719	0.001096	0.001292	0.000798	0.001024	0.001125	0.001125	0.000689	0.000818	0.000707
Photochemical oxidant formation [DALY]	0.000001	0.000001	0.000001	0.000001	0.000001	0.000001	0.000001	0.000001	0.000001	0.000001	0.000001	0.000001	0.0	0.000001	0.000001	0.000001	0.0	0.0
Terrestrial acidification [PDF*m2*year]	267.769429	268.162014	172.865856	293.634826	293.762072	181.706584	238.128207	199.439233	162.047583	199.70793	238.074319	155.79723	112.681324	207.998955	207.560191	156.381336	94.093131	79.567753
Thermally polluted water [PDF*m2*year]	0.002802	0.002802	0.001848	0.010734	0.010734	0.003418	0.00263	0.003024	0.001429	0.003024	0.00263	0.005296	0.015799	0.002348	0.002348	0.001154	0.007135	0.006019
Water availability, freshwater ecosystem [PDF*m2*year]	0.033535	0.033535	0.009507	0.073769	0.073769	0.017483	0.029524	0.026913	0.007431	0.026913	0.029524	0.024213	0.073692	0.025346	0.025346	0.00619	0.038751	0.032857
Water availability, human health [DALY]	0.005928	0.005928	0.000459	0.006874	0.006874	0.000684	0.005155	0.004083	0.000429	0.004083	0.005155	0.001215	0.004531	0.004379	0.004379	0.0004	0.003736	0.003124
Water availability, terrestrial ecosystem [PDF*m2*year]	0.888296	0.888296	0.049824	0.900956	0.900956	0.057966	0.768238	0.581427	0.04629	0.581427	0.768238	0.060674	0.215415	0.639471	0.639471	0.043992	0.188345	0.155945

Base

Medians_endpoint_all_results_Base=Medians_endpoint_all_results[Medians_endpoint_all_results['Scenario']=='Base']                                                         

Typical Mission

Medians_endpoint_all_results_Base_typical=Medians_endpoint_all_results_Base[Medians_endpoint_all_results_Base['Mission']=='Typical']
Base_typical=Medians_endpoint_all_results_Base_typical.pivot('Category and unit','Summary','Value')
Base_typical

C:\Users\kmadu\AppData\Local\Temp\ipykernel_17960\755782553.py:2: FutureWarning: In a future version of pandas all arguments of DataFrame.pivot will be keyword-only.
  Base_typical=Medians_endpoint_all_results_Base_typical.pivot('Category and unit','Summary','Value')

Summary	2030 Conventional (AAF)	2030 Conventional (Kerosene)	2030 GT-bat (AAF)	2030 GT-bat (Kerosene)	2040 Conventional (AAF)	2040 Conventional (Kerosene)	2040 GT-bat (AAF)	2040 GT-bat (Kerosene)	2040 PEMFC-bat (H2)	2050 Conventional (AAF)	2050 Conventional (Kerosene)	2050 PEMFC-bat (H2)	2050 SOFC-bat (H2)
Category and unit
Climate change, ecosystem quality, long term [PDF*m2*year]	415.491262	1207.108356	616.018199	1106.030626	384.792575	1104.784364	370.874967	779.380925	559.375101	319.375921	1053.642229	315.130666	280.217943
Climate change, ecosystem quality, short term [PDF*m2*year]	183.888645	352.61235	234.215542	326.1224	167.413121	323.546561	144.532607	232.739575	175.917011	137.046229	308.943903	103.562217	92.020284
Climate change, human health, long term [DALY]	0.001889	0.005498	0.002803	0.005037	0.001749	0.005033	0.001688	0.003549	0.002549	0.001452	0.004798	0.001435	0.001276
Climate change, human health, short term [DALY]	0.000851	0.00163	0.001083	0.001507	0.000775	0.001495	0.000669	0.001076	0.000813	0.000634	0.001428	0.000479	0.000425
Freshwater acidification [PDF*m2*year]	15.259835	12.181714	13.697693	10.356896	13.787071	11.365408	10.149669	8.763295	9.016261	12.211318	10.981907	6.950462	6.170377
Freshwater ecotoxicity, long term [PDF*m2*year]	23692.100384	20946.385997	29748.712077	24840.492304	24816.378905	21417.755181	28550.526353	26648.793708	44380.679735	26736.492477	21684.190239	44165.889823	39587.011987
Freshwater ecotoxicity, short term [PDF*m2*year]	26.41938	36.5062	30.762032	35.956742	26.506744	35.326461	28.289302	33.237482	35.302977	27.169965	35.919364	32.982613	29.912435
Freshwater eutrophication [PDF*m2*year]	0.912005	0.771932	0.786666	0.653595	0.774068	0.700202	0.485538	0.44177	0.382369	0.62261	0.68198	0.361239	0.292963
Human toxicity cancer, long term [DALY]	0.000008	0.000009	0.000011	0.00001	0.000008	0.000009	0.00001	0.00001	0.000016	0.000009	0.000008	0.000015	0.000013
Human toxicity cancer, short term [DALY]	0.000202	0.000149	0.00032	0.000224	0.000221	0.000153	0.000275	0.000236	0.000525	0.000265	0.000149	0.000512	0.000442
Human toxicity non-cancer, long term [DALY]	0.000265	0.00028	0.00035	0.000323	0.000278	0.000279	0.000321	0.000322	0.000521	0.000297	0.000275	0.000497	0.000449
Human toxicity non-cancer, short term [DALY]	0.000346	0.000307	0.000459	0.000387	0.000353	0.000307	0.000407	0.00038	0.000596	0.000363	0.000302	0.000564	0.00051
Ionizing radiation, ecosystem quality [PDF*m2*year]	0.0	0.0	0.000001	0.000001	0.0	0.0	0.0	0.0	0.000001	0.0	0.0	0.0	0.0
Ionizing radiation, human health [DALY]	0.000002	0.000003	0.000008	0.000006	0.000002	0.000003	0.000004	0.000004	0.000012	0.000002	0.000002	0.000005	0.000004
Land occupation, biodiversity [PDF*m2*year]	652.149029	21.023765	525.925159	23.511692	581.482286	21.947975	339.842901	24.957827	38.379626	530.546978	21.854149	35.458398	32.3392
Land transformation, biodiversity [PDF*m2*year]	71.6452	113.879264	91.867282	111.848521	73.820446	109.504415	79.898445	99.692027	138.549986	78.945529	106.0465	135.09771	116.082775
Marine acidification, long term [PDF*m2*year]	86.11602	291.398062	136.743187	266.403971	80.606012	266.549748	82.001788	187.371244	132.525583	67.279902	254.142184	73.604509	65.484368
Marine acidification, short term [PDF*m2*year]	9.348121	31.638873	14.843833	28.921751	8.749995	28.948351	8.90151	20.340562	14.391513	7.303411	27.590144	7.991182	7.109529
Marine eutrophication [PDF*m2*year]	3.629435	1.936707	2.958513	1.495842	3.316865	1.86752	1.987572	1.180827	1.131895	2.999981	1.826742	1.00907	0.899773
Ozone layer depletion [DALY]	0.0	0.000001	0.0	0.000001	0.0	0.000001	0.0	0.0	0.0	0.0	0.000001	0.0	0.0
Particulate matter formation [DALY]	0.000761	0.000454	0.000739	0.000453	0.00068	0.000425	0.000539	0.000394	0.000492	0.00059	0.0004	0.000384	0.000344
Photochemical oxidant formation [DALY]	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Terrestrial acidification [PDF*m2*year]	126.025494	84.661574	108.862876	69.835521	112.907821	79.03299	77.424284	58.181616	58.065791	98.36929	76.388396	44.283998	39.369399
Thermally polluted water [PDF*m2*year]	0.001949	0.001487	0.008131	0.004719	0.002221	0.001296	0.003721	0.003207	0.010776	0.001825	0.000886	0.004371	0.003644
Water availability, freshwater ecosystem [PDF*m2*year]	0.018875	0.008297	0.055373	0.02836	0.020362	0.007542	0.027373	0.020277	0.072285	0.018243	0.005454	0.034915	0.028986
Water availability, human health [DALY]	0.002794	0.000414	0.002932	0.000752	0.002492	0.000413	0.001829	0.000672	0.001923	0.002117	0.000368	0.00141	0.0012
Water availability, terrestrial ecosystem [PDF*m2*year]	0.406583	0.043644	0.356612	0.057451	0.357106	0.04364	0.228053	0.052405	0.113326	0.298146	0.040462	0.087168	0.076582

Design Mission

Medians_endpoint_all_results_Base_design=Medians_endpoint_all_results_Base[Medians_endpoint_all_results_Base['Mission']=='Design']
Medians_endpoint_all_results_Base_design=Medians_endpoint_all_results_Base[Medians_endpoint_all_results_Base['Mission']=='Design']
Base_design=Medians_endpoint_all_results_Base_design.pivot('Category and unit','Summary','Value')
Base_design

C:\Users\kmadu\AppData\Local\Temp\ipykernel_17960\857142569.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.pivot will be keyword-only.
  Base_design=Medians_endpoint_all_results_Base_design.pivot('Category and unit','Summary','Value')

Summary	2030 Conventional (AAF)	2030 Conventional (Kerosene)	2030 GT-bat (AAF)	2030 GT-bat (Kerosene)	2040 Conventional (AAF)	2040 Conventional (Kerosene)	2040 GT-bat (AAF)	2040 GT-bat (Kerosene)	2040 PEMFC-bat (H2)	2050 Conventional (AAF)	2050 Conventional (Kerosene)	2050 PEMFC-bat (H2)	2050 SOFC-bat (H2)
Category and unit
Climate change, ecosystem quality, long term [PDF*m2*year]	788.648172	2627.814451	1485.268064	2714.530247	703.78023	2387.560832	655.480818	2200.908272	1684.177378	587.458184	2298.49619	793.633858	685.078146
Climate change, ecosystem quality, short term [PDF*m2*year]	369.444663	759.283794	578.979158	788.950593	324.397918	691.144572	283.580709	646.460363	519.733564	265.851387	665.605316	253.122284	218.39431
Climate change, human health, long term [DALY]	0.003583	0.011972	0.006756	0.012362	0.003199	0.010873	0.002979	0.010022	0.007673	0.002671	0.010467	0.003614	0.003119
Climate change, human health, short term [DALY]	0.00171	0.003509	0.002679	0.003646	0.001501	0.003194	0.001312	0.002988	0.002402	0.00123	0.003076	0.00117	0.001009
Freshwater acidification [PDF*m2*year]	31.666462	24.560486	37.219734	26.07557	28.40143	22.929426	24.649488	22.895249	20.429903	25.041815	22.111458	14.81707	12.474759
Freshwater ecotoxicity, long term [PDF*m2*year]	28263.606434	21918.547038	46048.514722	26472.843408	29834.522769	21919.157751	32920.036856	39391.238569	101552.921259	34073.400265	22204.43253	93579.960181	79924.110429
Freshwater ecotoxicity, short term [PDF*m2*year]	39.682935	63.114919	56.915463	66.481923	38.133188	58.847242	37.999459	64.040888	78.547998	38.435221	58.48467	68.907681	59.014314
Freshwater eutrophication [PDF*m2*year]	2.080648	1.756363	2.174657	1.781594	1.76708	1.605666	1.36681	1.260366	1.245964	1.421042	1.560808	1.103215	0.873685
Human toxicity cancer, long term [DALY]	0.000008	0.00001	0.000017	0.000012	0.000009	0.00001	0.00001	0.000016	0.000039	0.00001	0.00001	0.000032	0.000027
Human toxicity cancer, short term [DALY]	0.000315	0.000192	0.000657	0.000281	0.000348	0.000188	0.000385	0.000524	0.00155	0.000455	0.000185	0.001492	0.001264
Human toxicity non-cancer, long term [DALY]	0.000283	0.000319	0.000523	0.000369	0.000308	0.000311	0.000349	0.000501	0.001205	0.000358	0.000307	0.001035	0.000883
Human toxicity non-cancer, short term [DALY]	0.00043	0.000338	0.000728	0.000442	0.000439	0.000331	0.000482	0.000561	0.001333	0.000468	0.000328	0.001144	0.000971
Ionizing radiation, ecosystem quality [PDF*m2*year]	0.0	0.000001	0.000002	0.000001	0.0	0.000001	0.0	0.000001	0.000003	0.0	0.000001	0.000001	0.000001
Ionizing radiation, human health [DALY]	0.000003	0.000006	0.000021	0.00001	0.000004	0.000006	0.000005	0.000016	0.000044	0.000004	0.000005	0.000018	0.000015
Land occupation, biodiversity [PDF*m2*year]	1485.927467	24.142948	1491.273941	27.272844	1325.790813	25.069491	996.809321	41.266566	90.574049	1210.921474	25.252309	76.18057	66.701784
Land transformation, biodiversity [PDF*m2*year]	117.17761	215.039307	190.86504	226.559519	118.013276	200.540147	118.436985	251.025574	408.501325	132.496947	194.880471	381.493384	321.598565
Marine acidification, long term [PDF*m2*year]	158.328634	635.011061	324.692505	655.261083	142.141243	576.815197	137.087184	529.617381	398.663922	119.561375	555.162323	184.473264	159.280658
Marine acidification, short term [PDF*m2*year]	17.18699	68.963319	35.246227	71.161498	15.429806	62.619826	14.881175	57.496394	43.310679	12.978702	60.275152	20.034654	17.291248
Marine eutrophication [PDF*m2*year]	8.251508	4.372859	9.233079	4.834307	7.543633	4.19023	6.219435	4.106708	2.870147	6.809575	4.09738	2.238387	1.88552
Ozone layer depletion [DALY]	0.0	0.000002	0.0	0.000002	0.0	0.000002	0.0	0.000001	0.0	0.0	0.000002	0.0	0.0
Particulate matter formation [DALY]	0.001516	0.000805	0.001806	0.000889	0.001325	0.000737	0.001136	0.000841	0.001123	0.001145	0.000702	0.00081	0.000695
Photochemical oxidant formation [DALY]	0.000001	0.000001	0.000001	0.000001	0.000001	0.000001	0.000001	0.000001	0.0	0.000001	0.000001	0.0	0.0
Terrestrial acidification [PDF*m2*year]	270.158605	174.506535	307.925569	185.375764	240.755324	163.249668	203.566311	161.337345	133.60317	209.447932	157.676173	94.793498	80.166978
Thermally polluted water [PDF*m2*year]	0.003387	0.002288	0.02122	0.005707	0.004022	0.001817	0.005288	0.011566	0.038877	0.003556	0.001364	0.014404	0.012059
Water availability, freshwater ecosystem [PDF*m2*year]	0.036839	0.011903	0.146537	0.032801	0.039894	0.009913	0.044424	0.075534	0.263524	0.037383	0.007565	0.117729	0.098563
Water availability, human health [DALY]	0.005983	0.000477	0.007718	0.000836	0.005273	0.00045	0.004276	0.001646	0.006109	0.004463	0.000401	0.004069	0.003385
Water availability, terrestrial ecosystem [PDF*m2*year]	0.889651	0.050743	0.951757	0.066801	0.773433	0.049127	0.597209	0.086301	0.306154	0.645117	0.046261	0.200746	0.167061

PkBudg500

Medians_endpoint_all_results_PkBudg500=Medians_endpoint_all_results[Medians_endpoint_all_results['Scenario']=='PkBudg500']                                                         

Typical Mission

Medians_endpoint_all_results_PkBudg500_typical=Medians_endpoint_all_results_PkBudg500[Medians_endpoint_all_results_PkBudg500['Mission']=='Typical']
PkBudg500_typical=Medians_endpoint_all_results_PkBudg500_typical.pivot('Category and unit','Summary','Value')
PkBudg500_typical

C:\Users\kmadu\AppData\Local\Temp\ipykernel_17960\438035318.py:2: FutureWarning: In a future version of pandas all arguments of DataFrame.pivot will be keyword-only.
  PkBudg500_typical=Medians_endpoint_all_results_PkBudg500_typical.pivot('Category and unit','Summary','Value')

Summary	2030 Conventional (AAF)	2030 Conventional (Kerosene)	2030 GT-bat (AAF)	2030 GT-bat (Kerosene)	2040 Conventional (AAF)	2040 Conventional (Kerosene)	2040 GT-bat (AAF)	2040 GT-bat (Kerosene)	2040 PEMFC-bat (H2)	2050 Conventional (AAF)	2050 Conventional (Kerosene)	2050 PEMFC-bat (H2)	2050 SOFC-bat (H2)
Category and unit
Climate change, ecosystem quality, long term [PDF*m2*year]	344.439021	1150.708536	358.138277	944.928681	281.235589	1041.256881	218.164804	648.063501	171.517005	222.890457	1004.372126	149.008257	134.617744
Climate change, ecosystem quality, short term [PDF*m2*year]	162.594933	334.829319	155.018083	276.164983	135.51537	303.951057	96.21758	192.480882	57.240301	109.616701	294.249007	51.944325	47.411891
Climate change, human health, long term [DALY]	0.001567	0.005241	0.001628	0.004305	0.001278	0.004742	0.000993	0.002951	0.000781	0.001013	0.004576	0.000679	0.000615
Climate change, human health, short term [DALY]	0.000753	0.001547	0.000717	0.001276	0.000627	0.001405	0.000445	0.00089	0.000265	0.000507	0.00136	0.00024	0.000219
Freshwater acidification [PDF*m2*year]	14.85757	11.867976	12.615356	9.653768	13.519788	11.217769	9.76082	8.43188	8.04648	12.152636	10.910336	6.911159	6.119861
Freshwater ecotoxicity, long term [PDF*m2*year]	23235.616718	20577.477187	28914.173167	24378.259523	24609.619546	21288.021982	28475.930863	26586.252797	43674.297178	26686.529006	21762.544631	44187.040912	39493.111114
Freshwater ecotoxicity, short term [PDF*m2*year]	23.246177	33.43631	26.406977	32.67682	24.875478	34.423064	24.752834	29.960448	28.051581	26.79393	35.729922	29.25089	28.149953
Freshwater eutrophication [PDF*m2*year]	0.917117	0.772466	0.791859	0.659521	0.779812	0.704299	0.495364	0.450631	0.385938	0.627097	0.681883	0.368713	0.303653
Human toxicity cancer, long term [DALY]	0.000007	0.000008	0.000009	0.000009	0.000008	0.000008	0.000009	0.000009	0.000015	0.000009	0.000008	0.000015	0.000013
Human toxicity cancer, short term [DALY]	0.0002	0.000148	0.000318	0.000224	0.000222	0.000153	0.000281	0.000242	0.000544	0.000272	0.000153	0.00053	0.000458
Human toxicity non-cancer, long term [DALY]	0.000257	0.000273	0.00032	0.000304	0.000271	0.000274	0.000313	0.000315	0.000496	0.000298	0.000276	0.000502	0.000451
Human toxicity non-cancer, short term [DALY]	0.000336	0.000298	0.000423	0.000364	0.000342	0.000299	0.000392	0.000368	0.000558	0.000358	0.000298	0.000556	0.000504
Ionizing radiation, ecosystem quality [PDF*m2*year]	0.0	0.0	0.000001	0.000001	0.0	0.0	0.0	0.0	0.000001	0.0	0.0	0.0	0.0
Ionizing radiation, human health [DALY]	0.000001	0.000003	0.000007	0.000006	0.000002	0.000003	0.000003	0.000004	0.000009	0.000002	0.000002	0.000004	0.000004
Land occupation, biodiversity [PDF*m2*year]	657.273158	22.931519	532.430711	27.58697	589.589179	25.141669	348.789018	30.565563	54.098897	546.735365	27.429017	48.95094	44.143116
Land transformation, biodiversity [PDF*m2*year]	73.081576	115.653282	101.874269	117.92834	74.66036	110.875062	84.68521	104.590592	155.1724	78.098547	105.658036	142.540224	124.653795
Marine acidification, long term [PDF*m2*year]	68.80692	277.7565	74.043585	227.505378	55.352294	251.193121	45.245873	155.657722	38.877194	43.973795	242.271521	33.596991	30.363628
Marine acidification, short term [PDF*m2*year]	7.469172	30.15668	8.037626	24.697192	6.008637	27.277802	4.911559	16.900506	4.220222	4.773471	26.306012	3.647042	3.296052
Marine eutrophication [PDF*m2*year]	3.582324	1.907864	2.883811	1.45172	3.301653	1.862943	1.978016	1.158286	1.055043	2.995696	1.844432	1.026167	0.941476
Ozone layer depletion [DALY]	0.0	0.000001	0.0	0.000001	0.0	0.000001	0.0	0.0	0.0	0.0	0.000001	0.0	0.0
Particulate matter formation [DALY]	0.000716	0.00042	0.00067	0.000401	0.000645	0.000399	0.000498	0.000357	0.000424	0.000579	0.000391	0.000377	0.000337
Photochemical oxidant formation [DALY]	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Terrestrial acidification [PDF*m2*year]	123.387978	82.577621	101.683879	65.299184	111.061117	77.969719	74.935445	55.914279	51.668118	97.896969	75.855891	44.284187	39.203271
Thermally polluted water [PDF*m2*year]	0.001271	0.000909	0.003582	0.002133	0.001191	0.000715	0.00164	0.00137	0.004058	0.001116	0.000621	0.002105	0.001803
Water availability, freshwater ecosystem [PDF*m2*year]	0.015443	0.005113	0.023386	0.010415	0.013572	0.004244	0.012544	0.007269	0.020579	0.012093	0.003851	0.012686	0.010971
Water availability, human health [DALY]	0.00275	0.000384	0.002668	0.000608	0.002414	0.000368	0.001731	0.000585	0.001667	0.002103	0.000373	0.001478	0.001279
Water availability, terrestrial ecosystem [PDF*m2*year]	0.403712	0.040919	0.336733	0.046037	0.351325	0.04035	0.219538	0.044053	0.088382	0.298485	0.040956	0.084654	0.075493

Design Mission

Medians_endpoint_all_results_PkBudg500_design=Medians_endpoint_all_results_PkBudg500[Medians_endpoint_all_results_PkBudg500['Mission']=='Design']
Medians_endpoint_all_results_PkBudg500_design=Medians_endpoint_all_results_PkBudg500[Medians_endpoint_all_results_PkBudg500['Mission']=='Design']
PkBudg500_design=Medians_endpoint_all_results_PkBudg500_design.pivot('Category and unit','Summary','Value')
PkBudg500_design

C:\Users\kmadu\AppData\Local\Temp\ipykernel_17960\1645133878.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.pivot will be keyword-only.
  PkBudg500_design=Medians_endpoint_all_results_PkBudg500_design.pivot('Category and unit','Summary','Value')

Summary	2030 Conventional (AAF)	2030 Conventional (Kerosene)	2030 GT-bat (AAF)	2030 GT-bat (Kerosene)	2040 Conventional (AAF)	2040 Conventional (Kerosene)	2040 GT-bat (AAF)	2040 GT-bat (Kerosene)	2040 PEMFC-bat (H2)	2050 Conventional (AAF)	2050 Conventional (Kerosene)	2050 PEMFC-bat (H2)	2050 SOFC-bat (H2)
Category and unit
Climate change, ecosystem quality, long term [PDF*m2*year]	671.198593	2546.458241	821.713167	2531.819757	524.173448	2310.934837	447.11551	1808.732893	365.006962	430.074805	2235.36658	309.194229	265.518609
Climate change, ecosystem quality, short term [PDF*m2*year]	331.776259	734.556651	376.064066	731.310036	270.867442	667.400484	220.790182	525.029292	116.827432	218.690986	647.535032	100.900612	88.618966
Climate change, human health, long term [DALY]	0.003048	0.011598	0.003734	0.011531	0.002381	0.010537	0.002031	0.008237	0.001664	0.001954	0.01018	0.001413	0.001211
Climate change, human health, short term [DALY]	0.001536	0.003395	0.001741	0.00338	0.001254	0.003084	0.001022	0.002427	0.00054	0.001013	0.002993	0.000466	0.00041
Freshwater acidification [PDF*m2*year]	31.081043	24.039294	34.518618	25.230748	28.052882	22.698943	24.128869	21.952999	17.023313	24.883886	22.047068	14.80311	12.434463
Freshwater ecotoxicity, long term [PDF*m2*year]	27592.562362	21422.357196	43867.152997	25850.886709	29433.333428	21735.944893	32790.821706	38860.582574	98500.282176	33868.137987	22234.550704	95111.025886	81286.347112
Freshwater ecotoxicity, short term [PDF*m2*year]	34.873568	59.064663	47.610189	61.506233	35.072678	57.459001	33.972235	57.688222	61.116608	36.369132	58.006996	58.608523	52.251955
Freshwater eutrophication [PDF*m2*year]	2.097111	1.75461	2.183071	1.784145	1.785157	1.607098	1.380234	1.276723	1.268772	1.43453	1.565495	1.138418	0.911218
Human toxicity cancer, long term [DALY]	0.000008	0.00001	0.000013	0.000011	0.000008	0.00001	0.00001	0.000015	0.000033	0.00001	0.00001	0.000032	0.000028
Human toxicity cancer, short term [DALY]	0.000312	0.00019	0.000653	0.000281	0.00035	0.000189	0.000395	0.000542	0.001611	0.000468	0.000189	0.001581	0.001325
Human toxicity non-cancer, long term [DALY]	0.000271	0.000309	0.000446	0.000348	0.000298	0.000306	0.000337	0.000472	0.001087	0.00036	0.000308	0.001051	0.000912
Human toxicity non-cancer, short term [DALY]	0.000415	0.000326	0.000637	0.000416	0.000423	0.000323	0.000463	0.000519	0.001174	0.00046	0.000322	0.001123	0.000981
Ionizing radiation, ecosystem quality [PDF*m2*year]	0.0	0.000001	0.000001	0.000001	0.0	0.000001	0.0	0.000001	0.000002	0.0	0.000001	0.000001	0.000001
Ionizing radiation, human health [DALY]	0.000002	0.000006	0.000019	0.000009	0.000003	0.000005	0.000004	0.000013	0.000034	0.000003	0.000005	0.000014	0.000012
Land occupation, biodiversity [PDF*m2*year]	1500.540662	27.254248	1499.388059	32.752366	1347.943393	30.132869	1011.121771	59.279252	143.817896	1245.445263	34.410929	117.585041	102.625729
Land transformation, biodiversity [PDF*m2*year]	119.109439	217.152567	217.65296	232.839318	118.072275	200.862553	122.953719	267.416224	463.686864	129.693561	194.437078	411.408682	354.130341
Marine acidification, long term [PDF*m2*year]	130.026283	615.432511	164.161672	611.176698	98.868893	558.450784	86.58912	434.963257	80.203682	81.378647	539.978008	67.313715	58.10472
Marine acidification, short term [PDF*m2*year]	14.114696	66.807082	17.820182	66.359016	10.732479	60.680695	9.399477	47.221807	8.706321	8.833867	58.624928	7.307081	6.30742
Marine eutrophication [PDF*m2*year]	8.193289	4.316892	9.045889	4.776261	7.518437	4.176407	6.188904	4.059177	2.638021	6.799504	4.085326	2.335045	2.000978
Ozone layer depletion [DALY]	0.0	0.000002	0.0	0.000002	0.0	0.000002	0.0	0.000001	0.0	0.0	0.000002	0.0	0.0
Particulate matter formation [DALY]	0.001444	0.000757	0.001652	0.000817	0.00128	0.000707	0.001084	0.000776	0.000961	0.001128	0.000691	0.000826	0.00071
Photochemical oxidant formation [DALY]	0.000001	0.000001	0.000001	0.000001	0.000001	0.000001	0.000001	0.000001	0.0	0.000001	0.000001	0.0	0.0
Terrestrial acidification [PDF*m2*year]	266.486161	171.206096	289.990889	179.796967	238.543799	161.644254	200.058684	155.19787	111.139447	207.76489	157.152922	95.332417	80.017251
Thermally polluted water [PDF*m2*year]	0.002369	0.001511	0.009269	0.002845	0.002272	0.001166	0.002593	0.004705	0.014479	0.00221	0.001057	0.006893	0.005889
Water availability, freshwater ecosystem [PDF*m2*year]	0.031527	0.00779	0.060588	0.013706	0.028111	0.006248	0.025257	0.022242	0.070775	0.024987	0.005766	0.039293	0.03347
Water availability, human health [DALY]	0.005918	0.000448	0.006971	0.000693	0.005176	0.000411	0.004153	0.001391	0.005217	0.004464	0.000412	0.004344	0.003687
Water availability, terrestrial ecosystem [PDF*m2*year]	0.888509	0.04815	0.89538	0.055749	0.772545	0.047031	0.587296	0.062724	0.220154	0.647582	0.047143	0.195259	0.164052

Midpoint dataframe

In this section we present dataframes with midpoint characterized results for each SSP scenario and mission type

Medians_midpoint_all_results=Medians_midpoint
Medians_midpoint_all_results['Summary']=Medians_midpoint_all_results['Year']+" "+Medians_midpoint_all_results['Configuration']+' ('+Medians_midpoint_all_results['Fuel']+')'

#Medians_midpoint_all_results=Medians_midpoint_all_results.drop(columns=['Year','Configuration','Fuel'])

NDC

Medians_midpoint_all_results_NDC=Medians_midpoint_all_results[Medians_midpoint_all_results['Scenario']=='NDC']                                                         

Typical Mission

Medians_midpoint_all_results_NDC_typical=Medians_midpoint_all_results_NDC[Medians_midpoint_all_results_NDC['Mission']=='Typical']
NDC_typical=Medians_midpoint_all_results_NDC_typical.pivot('Category and unit','Summary','Value').astype(float).style.format('{:.2E}')
NDC_typical

C:\Users\kmadu\AppData\Local\Temp\ipykernel_17960\1701558939.py:2: FutureWarning: In a future version of pandas all arguments of DataFrame.pivot will be keyword-only.
  NDC_typical=Medians_midpoint_all_results_NDC_typical.pivot('Category and unit','Summary','Value').astype(float).style.format('{:.2E}')

Summary	2030 Conventional (AAF)	2030 Conventional (ILUC)	2030 Conventional (Kerosene)	2030 GT-bat (AAF)	2030 GT-bat (ILUC)	2030 GT-bat (Kerosene)	2040 Conventional (AAF)	2040 Conventional (ILUC)	2040 Conventional (Kerosene)	2040 GT-bat (AAF)	2040 GT-bat (ILUC)	2040 GT-bat (Kerosene)	2040 PEMFC-bat (H2)	2050 Conventional (AAF)	2050 Conventional (ILUC)	2050 Conventional (Kerosene)	2050 PEMFC-bat (H2)	2050 SOFC-bat (H2)
Category and unit
Climate change, long term [kg CO2eq.]	8.67E+02	1.14E+03	1.89E+03	8.68E+02	1.09E+03	1.59E+03	7.24E+02	6.72E+02	1.71E+03	5.38E+02	9.62E+02	1.09E+03	3.61E+02	5.75E+02	7.52E+02	1.64E+03	2.89E+02	2.71E+02
Climate change, short term [kg CO2eq.]	9.74E+02	1.25E+03	1.93E+03	9.66E+02	1.18E+03	1.63E+03	8.19E+02	7.39E+02	1.75E+03	6.05E+02	1.06E+03	1.13E+03	3.98E+02	6.53E+02	8.30E+02	1.68E+03	3.23E+02	3.03E+02
Fossil and nuclear energy use [MJ dep.]	9.75E+03	9.75E+03	2.86E+04	1.51E+04	1.51E+04	2.65E+04	8.70E+03	8.53E+03	2.60E+04	8.53E+03	8.70E+03	1.82E+04	1.31E+04	7.09E+03	7.09E+03	2.46E+04	7.65E+03	6.76E+03
Freshwater acidification [kg SO2eq.]	1.86E-05	1.85E-05	1.52E-05	1.57E-05	1.57E-05	1.23E-05	1.68E-05	1.19E-05	1.42E-05	1.19E-05	1.68E-05	1.05E-05	9.73E-06	1.50E-05	1.51E-05	1.38E-05	8.11E-06	7.21E-06
Freshwater ecotoxicity [CTUe]	4.08E+07	4.08E+07	3.62E+07	5.07E+07	5.07E+07	4.28E+07	4.29E+07	4.97E+07	3.72E+07	4.97E+07	4.29E+07	4.66E+07	7.75E+07	4.64E+07	4.64E+07	3.77E+07	7.65E+07	6.93E+07
Freshwater eutrophication [kg PO4eq.]	7.99E-02	7.99E-02	6.79E-02	6.91E-02	6.91E-02	5.77E-02	6.82E-02	4.31E-02	6.17E-02	4.31E-02	6.82E-02	3.92E-02	3.44E-02	5.50E-02	5.50E-02	5.96E-02	3.20E-02	2.57E-02
Human toxicity cancer [CTUh]	7.42E-05	7.42E-05	5.59E-05	1.03E-04	1.03E-04	7.62E-05	7.84E-05	9.11E-05	5.68E-05	9.11E-05	7.84E-05	7.93E-05	1.56E-04	8.99E-05	8.99E-05	5.54E-05	1.49E-04	1.32E-04
Human toxicity non cancer [CTUh]	2.94E-04	2.94E-04	2.73E-04	3.66E-04	3.66E-04	3.20E-04	2.99E-04	3.41E-04	2.72E-04	3.41E-04	2.99E-04	3.27E-04	5.40E-04	3.14E-04	3.14E-04	2.70E-04	5.18E-04	4.67E-04
Ionizing radiations [Bq C14eq.]	7.29E+03	7.29E+03	1.51E+04	3.67E+04	3.67E+04	2.82E+04	8.77E+03	1.61E+04	1.34E+04	1.61E+04	8.77E+03	1.88E+04	4.88E+04	8.08E+03	8.08E+03	1.18E+04	2.21E+04	1.85E+04
Land occupation, biodiversity [m2eq*yr.]	9.32E+02	9.32E+02	3.24E+01	7.60E+02	7.60E+02	3.99E+01	8.33E+02	4.87E+02	3.28E+01	4.87E+02	8.33E+02	3.94E+01	6.66E+01	7.64E+02	7.64E+02	3.25E+01	5.78E+01	5.25E+01
Land transformation, biodiversity [m2eq.]	2.87E-01	2.87E-01	4.55E-01	3.93E-01	3.93E-01	4.60E-01	2.95E-01	3.31E-01	4.38E-01	3.31E-01	2.95E-01	4.11E-01	6.02E-01	3.16E-01	3.16E-01	4.23E-01	5.62E-01	4.89E-01
Marine eutrophication [kg Neq.]	2.88E-01	2.87E-01	1.53E-01	2.31E-01	2.30E-01	1.16E-01	2.63E-01	1.55E-01	1.48E-01	1.56E-01	2.63E-01	9.20E-02	8.76E-02	2.38E-01	2.37E-01	1.44E-01	8.10E-02	7.44E-02
Mineral resources use [kg dep.]	5.53E+01	5.53E+01	5.38E+01	5.67E+01	5.67E+01	5.51E+01	5.52E+01	5.57E+01	5.38E+01	5.57E+01	5.52E+01	5.49E+01	5.63E+01	5.52E+01	5.52E+01	5.38E+01	5.60E+01	5.60E+01
Ozone Layer Depletion [kg CFC-11eq.]	7.80E-05	7.80E-05	4.28E-04	7.85E-05	7.85E-05	3.44E-04	6.39E-05	4.94E-05	3.88E-04	4.94E-05	6.39E-05	2.30E-04	4.77E-05	4.83E-05	4.83E-05	3.74E-04	6.68E-05	5.27E-05
Particulate matter formation [kg PM2.5eq.]	6.18E-01	6.18E-01	3.67E-01	5.84E-01	5.84E-01	3.56E-01	5.46E-01	4.24E-01	3.40E-01	4.25E-01	5.47E-01	3.11E-01	3.76E-01	4.81E-01	4.81E-01	3.23E-01	3.10E-01	2.83E-01
Photochemical oxidant formation [kg NMVOCeq.]	8.44E+00	8.41E+00	7.50E+00	6.18E+00	6.17E+00	5.09E+00	7.75E+00	4.05E+00	7.04E+00	4.05E+00	7.73E+00	3.67E+00	2.00E+00	7.12E+00	7.11E+00	6.81E+00	1.61E+00	1.44E+00
Terrestrial acidification [kg SO2eq.]	1.90E-02	1.90E-02	1.27E-02	1.58E-02	1.58E-02	1.01E-02	1.69E-02	1.15E-02	1.19E-02	1.15E-02	1.70E-02	8.61E-03	7.98E-03	1.49E-02	1.49E-02	1.15E-02	6.64E-03	5.91E-03
Water scarcity [m3eq.]	1.48E+03	1.48E+03	1.51E+02	1.37E+03	1.37E+03	2.46E+02	1.28E+03	8.63E+02	1.50E+02	8.63E+02	1.28E+03	2.30E+02	5.98E+02	1.07E+03	1.07E+03	1.36E+02	4.99E+02	4.28E+02

Design Mission

Medians_midpoint_all_results_NDC_design=Medians_midpoint_all_results_NDC[Medians_midpoint_all_results_NDC['Mission']=='Design']
Medians_midpoint_all_results_NDC_design=Medians_midpoint_all_results_NDC[Medians_midpoint_all_results_NDC['Mission']=='Design']
NDC_design=Medians_midpoint_all_results_NDC_design.pivot('Category and unit','Summary','Value').astype(float).style.format('{:.2E}')
NDC_design

C:\Users\kmadu\AppData\Local\Temp\ipykernel_17960\1504253113.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.pivot will be keyword-only.
  NDC_design=Medians_midpoint_all_results_NDC_design.pivot('Category and unit','Summary','Value').astype(float).style.format('{:.2E}')

Summary	2030 Conventional (AAF)	2030 Conventional (ILUC)	2030 Conventional (Kerosene)	2030 GT-bat (AAF)	2030 GT-bat (ILUC)	2030 GT-bat (Kerosene)	2040 Conventional (AAF)	2040 Conventional (ILUC)	2040 Conventional (Kerosene)	2040 GT-bat (AAF)	2040 GT-bat (ILUC)	2040 GT-bat (Kerosene)	2040 PEMFC-bat (H2)	2050 Conventional (AAF)	2050 Conventional (ILUC)	2050 Conventional (Kerosene)	2050 PEMFC-bat (H2)	2050 SOFC-bat (H2)
Category and unit
Climate change, long term [kg CO2eq.]	1.75E+03	2.42E+03	4.14E+03	2.09E+03	2.74E+03	4.15E+03	1.43E+03	1.59E+03	3.75E+03	1.17E+03	2.00E+03	2.97E+03	7.54E+02	1.14E+03	7.52E+02	3.62E+03	5.81E+02	5.15E+02
Climate change, short term [kg CO2eq.]	1.97E+03	2.64E+03	4.21E+03	2.32E+03	2.97E+03	4.22E+03	1.62E+03	1.74E+03	3.81E+03	1.32E+03	2.19E+03	3.02E+03	8.05E+02	1.29E+03	8.30E+02	3.67E+03	6.29E+02	5.59E+02
Fossil and nuclear energy use [MJ dep.]	1.89E+04	1.89E+04	6.23E+04	3.71E+04	3.71E+04	6.45E+04	1.64E+04	1.52E+04	5.65E+04	1.52E+04	1.64E+04	5.21E+04	4.02E+04	1.35E+04	7.09E+03	5.40E+04	2.03E+04	1.74E+04
Freshwater acidification [kg SO2eq.]	3.90E-05	3.91E-05	3.12E-05	4.37E-05	4.37E-05	3.27E-05	3.51E-05	3.01E-05	2.92E-05	3.02E-05	3.50E-05	2.81E-05	2.08E-05	3.13E-05	1.51E-05	2.81E-05	1.75E-05	1.48E-05
Freshwater ecotoxicity [CTUe]	4.85E+07	4.85E+07	3.78E+07	7.71E+07	7.71E+07	4.55E+07	5.13E+07	5.71E+07	3.81E+07	5.71E+07	5.13E+07	6.80E+07	1.75E+08	5.89E+07	4.64E+07	3.88E+07	1.64E+08	1.41E+08
Freshwater eutrophication [kg PO4eq.]	1.83E-01	1.83E-01	1.54E-01	1.91E-01	1.91E-01	1.56E-01	1.55E-01	1.20E-01	1.41E-01	1.20E-01	1.55E-01	1.11E-01	1.11E-01	1.25E-01	5.50E-02	1.36E-01	9.91E-02	7.83E-02
Human toxicity cancer [CTUh]	1.14E-04	1.14E-04	7.19E-05	1.99E-04	1.99E-04	9.70E-05	1.20E-04	1.28E-04	7.02E-05	1.28E-04	1.20E-04	1.57E-04	4.27E-04	1.48E-04	8.99E-05	6.88E-05	4.02E-04	3.52E-04
Human toxicity non cancer [CTUh]	3.53E-04	3.53E-04	3.05E-04	5.52E-04	5.52E-04	3.68E-04	3.62E-04	3.97E-04	2.99E-04	3.97E-04	3.62E-04	4.77E-04	1.19E-03	3.99E-04	3.14E-04	2.97E-04	1.09E-03	9.37E-04
Ionizing radiations [Bq C14eq.]	1.20E+04	1.20E+04	3.01E+04	9.63E+04	9.63E+04	4.50E+04	1.57E+04	2.22E+04	2.66E+04	2.22E+04	1.57E+04	6.72E+04	1.78E+05	1.56E+04	8.08E+03	2.45E+04	7.47E+04	6.30E+04
Land occupation, biodiversity [m2eq*yr.]	2.13E+03	2.13E+03	3.83E+01	2.15E+03	2.15E+03	4.70E+01	1.90E+03	1.42E+03	3.81E+01	1.42E+03	1.90E+03	7.21E+01	1.71E+02	1.74E+03	7.64E+02	3.80E+01	1.34E+02	1.16E+02
Land transformation, biodiversity [m2eq.]	4.69E-01	4.69E-01	8.56E-01	8.34E-01	8.34E-01	9.15E-01	4.69E-01	4.83E-01	8.01E-01	4.83E-01	4.69E-01	1.05E+00	1.80E+00	5.26E-01	3.16E-01	7.75E-01	1.62E+00	1.38E+00
Marine eutrophication [kg Neq.]	6.58E-01	6.57E-01	3.47E-01	7.27E-01	7.29E-01	3.85E-01	5.99E-01	4.93E-01	3.34E-01	4.93E-01	5.97E-01	3.23E-01	2.15E-01	5.39E-01	2.37E-01	3.25E-01	1.82E-01	1.60E-01
Mineral resources use [kg dep.]	5.89E+01	5.89E+01	5.55E+01	6.30E+01	6.30E+01	5.75E+01	5.85E+01	5.86E+01	5.53E+01	5.86E+01	5.85E+01	5.82E+01	6.41E+01	5.85E+01	5.52E+01	5.53E+01	6.29E+01	6.19E+01
Ozone Layer Depletion [kg CFC-11eq.]	1.60E-04	1.60E-04	9.69E-04	1.92E-04	1.92E-04	9.50E-04	1.30E-04	1.07E-04	8.85E-04	1.07E-04	1.30E-04	6.77E-04	1.17E-04	9.77E-05	4.83E-05	8.53E-04	1.17E-04	9.34E-05
Particulate matter formation [kg PM2.5eq.]	1.23E+00	1.24E+00	6.57E-01	1.43E+00	1.43E+00	7.13E-01	1.08E+00	9.13E-01	6.00E-01	9.14E-01	1.08E+00	6.65E-01	8.54E-01	9.38E-01	4.81E-01	5.74E-01	6.84E-01	5.90E-01
Photochemical oxidant formation [kg NMVOCeq.]	1.95E+01	1.96E+01	1.74E+01	2.18E+01	2.19E+01	1.83E+01	1.81E+01	1.51E+01	1.64E+01	1.51E+01	1.80E+01	1.46E+01	4.93E+00	1.67E+01	7.11E+00	1.59E+01	3.68E+00	3.18E+00
Terrestrial acidification [kg SO2eq.]	4.09E-02	4.09E-02	2.62E-02	4.48E-02	4.48E-02	2.75E-02	3.63E-02	3.04E-02	2.45E-02	3.04E-02	3.63E-02	2.36E-02	1.73E-02	3.17E-02	1.49E-02	2.37E-02	1.44E-02	1.22E-02
Water scarcity [m3eq.]	3.25E+03	3.25E+03	1.82E+02	3.69E+03	3.69E+03	2.88E+02	2.79E+03	2.18E+03	1.67E+02	2.18E+03	2.79E+03	5.27E+02	1.82E+03	2.30E+03	1.07E+03	1.53E+02	1.42E+03	1.20E+03

Base

Medians_midpoint_all_results_Base=Medians_midpoint_all_results[Medians_midpoint_all_results['Scenario']=='Base']                                                         

Typical Mission

Medians_midpoint_all_results_Base_typical=Medians_midpoint_all_results_Base[Medians_midpoint_all_results_Base['Mission']=='Typical']
Base_typical=Medians_midpoint_all_results_Base_typical.pivot('Category and unit','Summary','Value').astype(float).style.format('{:.2E}')
Base_typical

C:\Users\kmadu\AppData\Local\Temp\ipykernel_17960\3265215117.py:2: FutureWarning: In a future version of pandas all arguments of DataFrame.pivot will be keyword-only.
  Base_typical=Medians_midpoint_all_results_Base_typical.pivot('Category and unit','Summary','Value').astype(float).style.format('{:.2E}')

Summary	2030 Conventional (AAF)	2030 Conventional (Kerosene)	2030 GT-bat (AAF)	2030 GT-bat (Kerosene)	2040 Conventional (AAF)	2040 Conventional (Kerosene)	2040 GT-bat (AAF)	2040 GT-bat (Kerosene)	2040 PEMFC-bat (H2)	2050 Conventional (AAF)	2050 Conventional (Kerosene)	2050 PEMFC-bat (H2)	2050 SOFC-bat (H2)
Category and unit
Climate change, long term [kg CO2eq.]	9.29E+02	1.95E+03	1.21E+03	1.79E+03	8.43E+02	1.79E+03	7.34E+02	1.27E+03	9.28E+02	6.90E+02	1.70E+03	5.31E+02	4.73E+02
Climate change, short term [kg CO2eq.]	1.04E+03	1.99E+03	1.32E+03	1.84E+03	9.45E+02	1.83E+03	8.17E+02	1.31E+03	9.93E+02	7.75E+02	1.75E+03	5.84E+02	5.20E+02
Fossil and nuclear energy use [MJ dep.]	1.06E+04	2.93E+04	2.02E+04	2.94E+04	1.04E+04	2.68E+04	1.14E+04	2.06E+04	2.22E+04	8.68E+03	2.53E+04	1.18E+04	1.02E+04
Freshwater acidification [kg SO2eq.]	1.89E-05	1.54E-05	1.67E-05	1.29E-05	1.71E-05	1.44E-05	1.24E-05	1.08E-05	1.09E-05	1.52E-05	1.39E-05	8.32E-06	7.40E-06
Freshwater ecotoxicity [CTUe]	4.14E+07	3.66E+07	5.20E+07	4.35E+07	4.34E+07	3.74E+07	5.00E+07	4.66E+07	7.78E+07	4.68E+07	3.79E+07	7.74E+07	6.94E+07
Freshwater eutrophication [kg PO4eq.]	8.00E-02	6.77E-02	6.90E-02	5.73E-02	6.79E-02	6.14E-02	4.26E-02	3.88E-02	3.35E-02	5.46E-02	5.98E-02	3.17E-02	2.57E-02
Human toxicity cancer [CTUh]	7.79E-05	5.89E-05	1.25E-04	8.83E-05	8.34E-05	5.92E-05	9.84E-05	8.50E-05	1.75E-04	9.30E-05	5.70E-05	1.54E-04	1.34E-04
Human toxicity non cancer [CTUh]	3.02E-04	2.79E-04	4.14E-04	3.48E-04	3.11E-04	2.79E-04	3.60E-04	3.41E-04	5.90E-04	3.22E-04	2.74E-04	5.29E-04	4.70E-04
Ionizing radiations [Bq C14eq.]	7.39E+03	1.53E+04	3.86E+04	2.91E+04	9.30E+03	1.35E+04	1.75E+04	1.99E+04	5.58E+04	8.71E+03	1.17E+04	2.53E+04	2.08E+04
Land occupation, biodiversity [m2eq*yr.]	9.31E+02	3.00E+01	7.51E+02	3.36E+01	8.30E+02	3.13E+01	4.85E+02	3.56E+01	5.48E+01	7.57E+02	3.12E+01	5.06E+01	4.62E+01
Land transformation, biodiversity [m2eq.]	2.83E-01	4.50E-01	3.63E-01	4.42E-01	2.92E-01	4.33E-01	3.16E-01	3.94E-01	5.48E-01	3.12E-01	4.19E-01	5.34E-01	4.59E-01
Marine eutrophication [kg Neq.]	2.90E-01	1.55E-01	2.37E-01	1.20E-01	2.65E-01	1.49E-01	1.59E-01	9.45E-02	9.06E-02	2.40E-01	1.46E-01	8.07E-02	7.20E-02
Mineral resources use [kg dep.]	5.53E+01	5.38E+01	5.67E+01	5.51E+01	5.52E+01	5.38E+01	5.55E+01	5.47E+01	5.60E+01	5.52E+01	5.38E+01	5.59E+01	5.60E+01
Ozone Layer Depletion [kg CFC-11eq.]	7.98E-05	4.29E-04	8.61E-05	3.47E-04	6.60E-05	3.88E-04	5.39E-05	2.34E-04	6.28E-05	5.02E-05	3.76E-04	7.52E-05	5.81E-05
Particulate matter formation [kg PM2.5eq.]	6.35E-01	3.79E-01	6.17E-01	3.77E-01	5.67E-01	3.54E-01	4.49E-01	3.29E-01	4.10E-01	4.92E-01	3.34E-01	3.20E-01	2.87E-01
Photochemical oxidant formation [kg NMVOCeq.]	8.56E+00	7.61E+00	6.67E+00	5.38E+00	7.88E+00	7.13E+00	4.31E+00	3.87E+00	2.73E+00	7.26E+00	6.86E+00	1.83E+00	1.63E+00
Terrestrial acidification [kg SO2eq.]	1.92E-02	1.28E-02	1.66E-02	1.06E-02	1.72E-02	1.20E-02	1.18E-02	8.86E-03	8.89E-03	1.50E-02	1.16E-02	6.78E-03	6.02E-03
Water scarcity [m3eq.]	1.49E+03	1.58E+02	1.48E+03	3.03E+02	1.30E+03	1.56E+02	9.09E+02	2.67E+02	7.53E+02	1.07E+03	1.36E+02	5.34E+02	4.53E+02

Design Mission

Medians_midpoint_all_results_Base_design=Medians_midpoint_all_results_Base[Medians_midpoint_all_results_Base['Mission']=='Design']
Medians_midpoint_all_results_Base_design=Medians_midpoint_all_results_Base[Medians_midpoint_all_results_Base['Mission']=='Design']
Base_design=Medians_midpoint_all_results_Base_design.pivot('Category and unit','Summary','Value').astype(float).style.format('{:.2E}')
Base_design

C:\Users\kmadu\AppData\Local\Temp\ipykernel_17960\263758391.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.pivot will be keyword-only.
  Base_design=Medians_midpoint_all_results_Base_design.pivot('Category and unit','Summary','Value').astype(float).style.format('{:.2E}')

Summary	2030 Conventional (AAF)	2030 Conventional (Kerosene)	2030 GT-bat (AAF)	2030 GT-bat (Kerosene)	2040 Conventional (AAF)	2040 Conventional (Kerosene)	2040 GT-bat (AAF)	2040 GT-bat (Kerosene)	2040 PEMFC-bat (H2)	2050 Conventional (AAF)	2050 Conventional (Kerosene)	2050 PEMFC-bat (H2)	2050 SOFC-bat (H2)
Category and unit
Climate change, long term [kg CO2eq.]	1.86E+03	4.22E+03	3.00E+03	4.37E+03	1.64E+03	3.84E+03	1.44E+03	3.56E+03	2.77E+03	1.34E+03	3.69E+03	1.33E+03	1.15E+03
Climate change, short term [kg CO2eq.]	2.09E+03	4.29E+03	3.27E+03	4.46E+03	1.83E+03	3.90E+03	1.60E+03	3.65E+03	2.94E+03	1.50E+03	3.76E+03	1.43E+03	1.23E+03
Fossil and nuclear energy use [MJ dep.]	2.03E+04	6.34E+04	5.05E+04	6.77E+04	1.92E+04	5.73E+04	1.92E+04	6.16E+04	7.36E+04	1.66E+04	5.50E+04	3.36E+04	2.86E+04
Freshwater acidification [kg SO2eq.]	3.94E-05	3.14E-05	4.64E-05	3.34E-05	3.55E-05	2.94E-05	3.08E-05	2.91E-05	2.47E-05	3.15E-05	2.84E-05	1.78E-05	1.50E-05
Freshwater ecotoxicity [CTUe]	4.94E+07	3.84E+07	8.07E+07	4.63E+07	5.22E+07	3.84E+07	5.77E+07	6.91E+07	1.78E+08	5.97E+07	3.89E+07	1.64E+08	1.40E+08
Freshwater eutrophication [kg PO4eq.]	1.83E-01	1.54E-01	1.91E-01	1.56E-01	1.55E-01	1.41E-01	1.20E-01	1.11E-01	1.09E-01	1.25E-01	1.37E-01	9.68E-02	7.66E-02
Human toxicity cancer [CTUh]	1.20E-04	7.59E-05	2.56E-04	1.10E-04	1.29E-04	7.28E-05	1.38E-04	1.78E-04	4.97E-04	1.54E-04	7.07E-05	4.15E-04	3.54E-04
Human toxicity non cancer [CTUh]	3.65E-04	3.14E-04	6.78E-04	3.98E-04	3.81E-04	3.06E-04	4.22E-04	5.33E-04	1.39E-03	4.12E-04	3.02E-04	1.11E-03	9.49E-04
Ionizing radiations [Bq C14eq.]	1.22E+04	3.01E+04	1.02E+05	4.63E+04	1.69E+04	2.64E+04	2.42E+04	7.41E+04	2.08E+05	1.72E+04	2.43E+04	8.70E+04	7.26E+04
Land occupation, biodiversity [m2eq*yr.]	2.12E+03	3.45E+01	2.13E+03	3.89E+01	1.89E+03	3.58E+01	1.42E+03	5.89E+01	1.29E+02	1.73E+03	3.60E+01	1.09E+02	9.52E+01
Land transformation, biodiversity [m2eq.]	4.63E-01	8.50E-01	7.54E-01	8.96E-01	4.67E-01	7.93E-01	4.68E-01	9.92E-01	1.62E+00	5.24E-01	7.70E-01	1.51E+00	1.27E+00
Marine eutrophication [kg Neq.]	6.60E-01	3.50E-01	7.38E-01	3.87E-01	6.03E-01	3.35E-01	4.97E-01	3.29E-01	2.30E-01	5.45E-01	3.28E-01	1.79E-01	1.51E-01
Mineral resources use [kg dep.]	5.90E+01	5.56E+01	6.30E+01	5.76E+01	5.86E+01	5.53E+01	5.85E+01	5.79E+01	6.35E+01	5.86E+01	5.53E+01	6.26E+01	6.15E+01
Ozone Layer Depletion [kg CFC-11eq.]	1.64E-04	9.71E-04	2.13E-04	9.58E-04	1.33E-04	8.85E-04	1.13E-04	6.92E-04	1.73E-04	1.02E-04	8.57E-04	1.42E-04	1.14E-04
Particulate matter formation [kg PM2.5eq.]	1.26E+00	6.71E-01	1.50E+00	7.41E-01	1.11E+00	6.14E-01	9.47E-01	7.01E-01	9.36E-01	9.55E-01	5.85E-01	6.79E-01	5.80E-01
Photochemical oxidant formation [kg NMVOCeq.]	1.97E+01	1.75E+01	2.31E+01	1.87E+01	1.83E+01	1.66E+01	1.54E+01	1.54E+01	7.46E+00	1.69E+01	1.60E+01	4.37E+00	3.71E+00
Terrestrial acidification [kg SO2eq.]	4.12E-02	2.64E-02	4.70E-02	2.81E-02	3.67E-02	2.47E-02	3.10E-02	2.44E-02	2.04E-02	3.19E-02	2.39E-02	1.45E-02	1.23E-02
Water scarcity [m3eq.]	3.26E+03	1.88E+02	3.99E+03	3.42E+02	2.83E+03	1.72E+02	2.26E+03	6.78E+02	2.40E+03	2.32E+03	1.50E+02	1.53E+03	1.28E+03

PkBudg500

Medians_midpoint_all_results_PkBudg500=Medians_midpoint_all_results[Medians_midpoint_all_results['Scenario']=='PkBudg500']                                                         

Typical Mission

Medians_midpoint_all_results_PkBudg500_typical=Medians_midpoint_all_results_PkBudg500[Medians_midpoint_all_results_PkBudg500['Mission']=='Typical']
PkBudg500_typical=Medians_midpoint_all_results_PkBudg500_typical.pivot('Category and unit','Summary','Value').astype(float).style.format('{:.2E}')
PkBudg500_typical

C:\Users\kmadu\AppData\Local\Temp\ipykernel_17960\1610387013.py:2: FutureWarning: In a future version of pandas all arguments of DataFrame.pivot will be keyword-only.
  PkBudg500_typical=Medians_midpoint_all_results_PkBudg500_typical.pivot('Category and unit','Summary','Value').astype(float).style.format('{:.2E}')

Summary	2030 Conventional (AAF)	2030 Conventional (Kerosene)	2030 GT-bat (AAF)	2030 GT-bat (Kerosene)	2040 Conventional (AAF)	2040 Conventional (Kerosene)	2040 GT-bat (AAF)	2040 GT-bat (Kerosene)	2040 PEMFC-bat (H2)	2050 Conventional (AAF)	2050 Conventional (Kerosene)	2050 PEMFC-bat (H2)	2050 SOFC-bat (H2)
Category and unit
Climate change, long term [kg CO2eq.]	8.17E+02	1.85E+03	7.88E+02	1.52E+03	6.73E+02	1.68E+03	4.81E+02	1.05E+03	2.93E+02	5.33E+02	1.62E+03	2.59E+02	2.36E+02
Climate change, short term [kg CO2eq.]	9.19E+02	1.89E+03	8.77E+02	1.56E+03	7.66E+02	1.72E+03	5.44E+02	1.09E+03	3.24E+02	6.20E+02	1.66E+03	2.93E+02	2.68E+02
Fossil and nuclear energy use [MJ dep.]	9.18E+03	2.82E+04	1.37E+04	2.56E+04	8.17E+03	2.56E+04	7.81E+03	1.76E+04	1.16E+04	6.70E+03	2.45E+04	7.16E+03	6.36E+03
Freshwater acidification [kg SO2eq.]	1.84E-05	1.50E-05	1.54E-05	1.20E-05	1.67E-05	1.42E-05	1.19E-05	1.04E-05	9.64E-06	1.51E-05	1.38E-05	8.28E-06	7.33E-06
Freshwater ecotoxicity [CTUe]	4.06E+07	3.60E+07	5.06E+07	4.27E+07	4.30E+07	3.72E+07	4.98E+07	4.65E+07	7.66E+07	4.67E+07	3.80E+07	7.75E+07	6.92E+07
Freshwater eutrophication [kg PO4eq.]	8.04E-02	6.78E-02	6.95E-02	5.79E-02	6.84E-02	6.18E-02	4.35E-02	3.95E-02	3.39E-02	5.50E-02	5.98E-02	3.23E-02	2.66E-02
Human toxicity cancer [CTUh]	7.27E-05	5.47E-05	1.03E-04	7.57E-05	7.80E-05	5.62E-05	9.18E-05	7.93E-05	1.55E-04	9.15E-05	5.64E-05	1.55E-04	1.36E-04
Human toxicity non cancer [CTUh]	2.88E-04	2.68E-04	3.62E-04	3.18E-04	2.97E-04	2.70E-04	3.40E-04	3.25E-04	5.37E-04	3.16E-04	2.71E-04	5.27E-04	4.66E-04
Ionizing radiations [Bq C14eq.]	7.03E+03	1.49E+04	3.46E+04	2.70E+04	8.16E+03	1.32E+04	1.48E+04	1.74E+04	4.39E+04	7.48E+03	1.17E+04	1.99E+04	1.70E+04
Land occupation, biodiversity [m2eq*yr.]	9.38E+02	3.27E+01	7.60E+02	3.94E+01	8.41E+02	3.59E+01	4.98E+02	4.36E+01	7.72E+01	7.80E+02	3.91E+01	6.99E+01	6.30E+01
Land transformation, biodiversity [m2eq.]	2.89E-01	4.57E-01	4.03E-01	4.66E-01	2.95E-01	4.39E-01	3.35E-01	4.13E-01	6.13E-01	3.09E-01	4.18E-01	5.64E-01	4.93E-01
Marine eutrophication [kg Neq.]	2.87E-01	1.53E-01	2.31E-01	1.16E-01	2.64E-01	1.49E-01	1.58E-01	9.27E-02	8.44E-02	2.40E-01	1.48E-01	8.21E-02	7.53E-02
Mineral resources use [kg dep.]	5.53E+01	5.38E+01	5.67E+01	5.51E+01	5.52E+01	5.38E+01	5.56E+01	5.48E+01	5.62E+01	5.52E+01	5.38E+01	5.59E+01	5.59E+01
Ozone Layer Depletion [kg CFC-11eq.]	7.70E-05	4.27E-04	7.57E-05	3.42E-04	6.24E-05	3.88E-04	4.88E-05	2.31E-04	4.41E-05	4.71E-05	3.74E-04	6.50E-05	5.20E-05
Particulate matter formation [kg PM2.5eq.]	5.96E-01	3.50E-01	5.58E-01	3.35E-01	5.38E-01	3.32E-01	4.15E-01	2.98E-01	3.54E-01	4.83E-01	3.26E-01	3.15E-01	2.81E-01
Photochemical oxidant formation [kg NMVOCeq.]	8.34E+00	7.44E+00	6.03E+00	5.00E+00	7.69E+00	7.00E+00	3.99E+00	3.61E+00	1.88E+00	7.12E+00	6.80E+00	1.61E+00	1.42E+00
Terrestrial acidification [kg SO2eq.]	1.88E-02	1.25E-02	1.56E-02	9.93E-03	1.69E-02	1.18E-02	1.15E-02	8.52E-03	7.90E-03	1.49E-02	1.15E-02	6.78E-03	6.00E-03
Water scarcity [m3eq.]	1.47E+03	1.49E+02	1.39E+03	2.53E+02	1.28E+03	1.44E+02	8.75E+02	2.39E+02	6.57E+02	1.07E+03	1.41E+02	5.61E+02	4.83E+02

Design Mission

Medians_midpoint_all_results_PkBudg500_design=Medians_midpoint_all_results_PkBudg500[Medians_midpoint_all_results_PkBudg500['Mission']=='Design']
Medians_midpoint_all_results_PkBudg500_design=Medians_midpoint_all_results_PkBudg500[Medians_midpoint_all_results_PkBudg500['Mission']=='Design']
PkBudg500_design=Medians_midpoint_all_results_PkBudg500_design.pivot('Category and unit','Summary','Value').astype(float).style.format('{:.2E}')
PkBudg500_design

C:\Users\kmadu\AppData\Local\Temp\ipykernel_17960\2003282372.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.pivot will be keyword-only.
  PkBudg500_design=Medians_midpoint_all_results_PkBudg500_design.pivot('Category and unit','Summary','Value').astype(float).style.format('{:.2E}')

Summary	2030 Conventional (AAF)	2030 Conventional (Kerosene)	2030 GT-bat (AAF)	2030 GT-bat (Kerosene)	2040 Conventional (AAF)	2040 Conventional (Kerosene)	2040 GT-bat (AAF)	2040 GT-bat (Kerosene)	2040 PEMFC-bat (H2)	2050 Conventional (AAF)	2050 Conventional (Kerosene)	2050 PEMFC-bat (H2)	2050 SOFC-bat (H2)
Category and unit
Climate change, long term [kg CO2eq.]	1.67E+03	4.09E+03	1.91E+03	4.07E+03	1.35E+03	3.71E+03	1.10E+03	2.91E+03	6.16E+02	1.08E+03	3.59E+03	5.25E+02	4.56E+02
Climate change, short term [kg CO2eq.]	1.88E+03	4.15E+03	2.13E+03	4.13E+03	1.53E+03	3.77E+03	1.25E+03	2.97E+03	6.60E+02	1.24E+03	3.66E+03	5.70E+02	5.01E+02
Fossil and nuclear energy use [MJ dep.]	1.80E+04	6.16E+04	3.38E+04	6.32E+04	1.56E+04	5.57E+04	1.42E+04	5.08E+04	3.60E+04	1.27E+04	5.41E+04	1.87E+04	1.62E+04
Freshwater acidification [kg SO2eq.]	3.87E-05	3.08E-05	4.31E-05	3.24E-05	3.51E-05	2.91E-05	3.02E-05	2.79E-05	2.05E-05	3.13E-05	2.83E-05	1.78E-05	1.49E-05
Freshwater ecotoxicity [CTUe]	4.82E+07	3.75E+07	7.69E+07	4.53E+07	5.15E+07	3.80E+07	5.74E+07	6.83E+07	1.73E+08	5.93E+07	3.89E+07	1.67E+08	1.43E+08
Freshwater eutrophication [kg PO4eq.]	1.84E-01	1.54E-01	1.91E-01	1.57E-01	1.57E-01	1.41E-01	1.21E-01	1.12E-01	1.11E-01	1.26E-01	1.37E-01	9.99E-02	7.99E-02
Human toxicity cancer [CTUh]	1.12E-04	7.03E-05	2.00E-04	9.61E-05	1.20E-04	6.92E-05	1.28E-04	1.59E-04	4.31E-04	1.51E-04	6.93E-05	4.27E-04	3.59E-04
Human toxicity non cancer [CTUh]	3.45E-04	2.99E-04	5.45E-04	3.63E-04	3.59E-04	2.96E-04	3.95E-04	4.75E-04	1.17E-03	4.00E-04	2.97E-04	1.10E-03	9.48E-04
Ionizing radiations [Bq C14eq.]	1.16E+04	2.96E+04	9.14E+04	4.37E+04	1.45E+04	2.60E+04	2.04E+04	6.18E+04	1.61E+05	1.45E+04	2.43E+04	6.74E+04	5.81E+04
Land occupation, biodiversity [m2eq*yr.]	2.14E+03	3.89E+01	2.14E+03	4.67E+01	1.92E+03	4.30E+01	1.44E+03	8.46E+01	2.05E+02	1.78E+03	4.91E+01	1.68E+02	1.46E+02
Land transformation, biodiversity [m2eq.]	4.71E-01	8.58E-01	8.61E-01	9.20E-01	4.67E-01	7.94E-01	4.86E-01	1.06E+00	1.83E+00	5.13E-01	7.69E-01	1.63E+00	1.40E+00
Marine eutrophication [kg Neq.]	6.55E-01	3.45E-01	7.24E-01	3.82E-01	6.01E-01	3.34E-01	4.95E-01	3.25E-01	2.11E-01	5.44E-01	3.27E-01	1.87E-01	1.60E-01
Mineral resources use [kg dep.]	5.90E+01	5.55E+01	6.31E+01	5.76E+01	5.85E+01	5.53E+01	5.85E+01	5.80E+01	6.37E+01	5.85E+01	5.53E+01	6.28E+01	6.17E+01
Ozone Layer Depletion [kg CFC-11eq.]	1.59E-04	9.67E-04	1.85E-04	9.48E-04	1.27E-04	8.82E-04	1.06E-04	6.77E-04	1.08E-04	9.46E-05	8.56E-04	1.13E-04	9.05E-05
Particulate matter formation [kg PM2.5eq.]	1.20E+00	6.31E-01	1.38E+00	6.81E-01	1.07E+00	5.90E-01	9.03E-01	6.47E-01	8.01E-01	9.40E-01	5.76E-01	6.89E-01	5.92E-01
Photochemical oxidant formation [kg NMVOCeq.]	1.94E+01	1.73E+01	2.15E+01	1.82E+01	1.80E+01	1.64E+01	1.50E+01	1.45E+01	4.59E+00	1.67E+01	1.59E+01	3.65E+00	3.11E+00
Terrestrial acidification [kg SO2eq.]	4.07E-02	2.59E-02	4.42E-02	2.72E-02	3.64E-02	2.45E-02	3.05E-02	2.35E-02	1.70E-02	3.17E-02	2.38E-02	1.46E-02	1.23E-02
Water scarcity [m3eq.]	3.24E+03	1.78E+02	3.72E+03	2.92E+02	2.80E+03	1.62E+02	2.21E+03	5.89E+02	2.06E+03	2.33E+03	1.58E+02	1.64E+03	1.40E+03

Visualisations for article

Figure 2 Boxplot of endpoint results

(only for the endpoint baseline scenario and typical mission, no alternative aviation fuels are considered)

endpoints_NDC=endpoints_ST[endpoints_ST['Scenario']=='NDC']
endpoints_NDC_typical=endpoints_NDC[endpoints_NDC['Mission']=='Typical']
endpoints_NDC_typical_kero_h2=endpoints_NDC_typical[(endpoints_NDC_typical["Fuel"] != "AAF") & (endpoints_NDC_typical["Fuel"] != "ILUC")]

g=sns.catplot(endpoints_NDC_typical_kero_h2,x='Configuration',y='Value',row='Damage',col='Year',hue='Scenario',sharey='row',margin_titles=True, sharex=False,kind='box', units='Unit', height=3.5, aspect=1.1,legend=None)
g.set_axis_labels('', '') 
g.axes[0,0].set_ylabel("Ecosystem Quality [PDF*m2*year/FU]")
g.axes[0,0].set_title("2030")
g.axes[0,1].set_title("2040")
g.axes[0,2].set_title("2050")
g.axes[1,0].set_ylabel("Human Health [DALY/FU]")
g.axes[1,0].set_xlabel('')
g.axes[1,1].set_xlabel('')
g.axes[1,2].set_xlabel('')

#some outliers are excluded due to visibility considerations, the same figure with all outliers is presented in the supporting word file. To obtain that figure with all outliers the next two lines need to be outcommented.
g.axes[0,0].set_ylim(0, 1200)
g.axes[1,0].set_ylim(0, 0.007)


g.axes[1,0].yaxis.set_major_formatter(lambda x, pos: "{:.0E}".format(x))

for i, ax in enumerate(g.axes.flat):
    plt.setp(ax.texts, text='')

plt.savefig("F2_boxplot_endpoint_NDC.svg", format='svg')
#plt.savefig("SI_F2_boxplot_endpoint_NDC.svg", format='svg')

Figure 3 Contribution analysis on damage level

(only for the endpoint baseline scenario and typical mission, no alternative aviation fuels are considered)

#Selecting only short-term damages (see method import section for more detail)
cols_HH_ST = ["Climate change, human health, short term",'Water availability, human health','Photochemical oxidant formation','Ionizing radiation, human health','Ozone layer depletion','Human toxicity cancer, short term','Human toxicity non-cancer, short term','Particulate matter formation']
cols_EQ_ST =['Climate change, ecosystem quality, short term','Marine acidification, short term','Land occupation, biodiversity','Water availability, freshwater ecosystem','Water availability, terrestrial ecosystem','Thermally polluted water','Freshwater ecotoxicity, short term','Freshwater acidification','Terrestrial acidification','Freshwater eutrophication','Marine eutrophication','Land transformation, biodiversity','Ionizing radiation, ecosystem quality']

deterministic_endpoint_2030_renamed=deterministic_endpoint_typ_2030[0].rename(index={'typ_aircraft_conv_kero_NDC': 'Conventional', 'typ_aircraft_GT_bat_kero_NDC': 'GT-bat'})
deterministic_endpoint_2040_renamed=deterministic_endpoint_typ_2040[0].rename(index={'typ_aircraft_conv_kero_NDC': 'Conventional', 'typ_aircraft_GT_bat_kero_NDC': 'GT-bat','typ_aircraft_PEMFC_bat_NDC': 'PEMFC-bat'})
deterministic_endpoint_2050_renamed=deterministic_endpoint_typ_2050[0].rename(index={'typ_aircraft_conv_kero_NDC': 'Conventional','typ_aircraft_PEMFC_bat_NDC': 'PEMFC-bat','typ_aircraft_SOFC_bat_NDC': 'SOFC-bat'})

endpoint_HH_2030_ST=deterministic_endpoint_2030_renamed[cols_HH_ST]
endpoint_EQ_2030_ST=deterministic_endpoint_2030_renamed[cols_EQ_ST]

endpoint_HH_2040_ST=deterministic_endpoint_2040_renamed[cols_HH_ST]
endpoint_EQ_2040_ST=deterministic_endpoint_2040_renamed[cols_EQ_ST]

endpoint_HH_2050_ST=deterministic_endpoint_2050_renamed[cols_HH_ST]
endpoint_EQ_2050_ST=deterministic_endpoint_2050_renamed[cols_EQ_ST]

configs_2030=['Conventional','GT-bat']
configs_2040=['Conventional','GT-bat','PEMFC-bat']
configs_2050=['Conventional','PEMFC-bat','SOFC-bat']

endpoint_HH_2030_ST_NDC=endpoint_HH_2030_ST.loc[configs_2030]
endpoint_EQ_2030_ST_NDC=endpoint_EQ_2030_ST.loc[configs_2030]
endpoint_HH_2040_ST_NDC=endpoint_HH_2040_ST.loc[configs_2040]
endpoint_EQ_2040_ST_NDC=endpoint_EQ_2040_ST.loc[configs_2040]
endpoint_HH_2050_ST_NDC=endpoint_HH_2050_ST.loc[configs_2050]
endpoint_EQ_2050_ST_NDC=endpoint_EQ_2050_ST.loc[configs_2050]

#setting color palette
palette=sns.color_palette('tab20')[:13]
sns.color_palette(palette)

# create stacked bar chart for ecosystem quality

#define subplot layout
fig, axes = plt.subplots(nrows=1, ncols=3, sharey=True, figsize=(9,5))

#add DataFrames to subplots
endpoint_EQ_2030_ST_NDC.plot(ax=axes[0],kind='bar', stacked=True, color=palette, fontsize=10, title='2030', legend=False)
endpoint_EQ_2040_ST_NDC.plot(ax=axes[1],kind='bar', stacked=True, color=palette, fontsize=10, title='2040',legend=False)
endpoint_EQ_2050_ST_NDC.plot(ax=axes[2],kind='bar', stacked=True, color=palette, fontsize=10, title='2050',legend=False)

handles, labels = axes[2].get_legend_handles_labels()
axes[0].set_ylabel('Ecosystem Quality [PDF*m2*year/FU]')
fig.legend(handles, labels,bbox_to_anchor=[1.15, 0.9],loc='upper center', frameon=False,fontsize=8)
sns.move_legend(fig, "lower center",bbox_to_anchor=(.5, -0.42), ncol=2,frameon=False)
axes[0].text(-0.05, 1.05, 'A', transform=axes[0].transAxes, size=20, weight='bold')

plt.savefig("F3A_contribution_damages_deterministic_EQ.svg", format='svg',bbox_inches='tight')

#setting color palette
alette=sns.color_palette('tab20')[:8]
sns.color_palette(palette)

# create stacked bar chart for human health

#define subplot layout
fig, axes = plt.subplots(nrows=1, ncols=3, sharey=True,figsize=(8,5))

#add DataFrames to subplots
endpoint_HH_2030_ST_NDC.plot(ax=axes[0],kind='bar', stacked=True, color=palette, fontsize=10, title='2030', legend=False)
endpoint_HH_2040_ST_NDC.plot(ax=axes[1],kind='bar', stacked=True, color=palette, fontsize=10, title='2040',legend=False)
endpoint_HH_2050_ST_NDC.plot(ax=axes[2],kind='bar', stacked=True, color=palette, fontsize=10, title='2050',legend=False)

handles, labels = axes[2].get_legend_handles_labels()
axes[0].set_ylabel('Human Health [DALY/FU]')
fig.legend(handles, labels,bbox_to_anchor=[1.13, 0.9],loc='upper center',frameon=False)
sns.move_legend(fig, "lower center",bbox_to_anchor=(.5, -0.30), ncol=2,frameon=False)
axes[0].text(-0.05, 1.05, 'B', transform=axes[0].transAxes, size=20, weight='bold')
axes[0].yaxis.set_major_formatter(lambda x, pos: "{:.0E}".format(x))

plt.savefig("F3B_contribution_damages_deterministic_HH.svg", format='svg',bbox_inches='tight')

Figure 4 Contribution Analysis: life cycle stages

based on deterministic results, obtained in

#setting color palette
palette=sns.color_palette('tab20')[:6]
sns.color_palette(palette)

Human health

#merging all life cycle stages dataframes for human health damages into one

contributions_HH=pd.concat([endpoint_stages_contribution_HH_2030,endpoint_stages_contribution_HH_2040,endpoint_stages_contribution_HH_2050])
contributions_HH=contributions_HH[['Mission','Configuration','Fuel','Airport use','Aircraft production','SAF production', 'Combustion','Kerosene production','Electricity production','H2','Year']]
contributions_HH=contributions_HH.rename({'H2':'Hydrogen production'}, axis='columns')

#summing all impacts to obtain the human health damage
contributions_HH['Sum']=contributions_HH.sum(axis = 1, numeric_only=True)

#2030
contributions_HH_2030_kerosene_typ=contributions_HH[((contributions_HH['Configuration']=='Conventional')|(contributions_HH['Configuration']=='GT-bat'))&(contributions_HH['Fuel']=='Kerosene') & (contributions_HH['Mission'] =='typical mission')&(contributions_HH['Year'] =='2030')]
contributions_HH_2030_kerosene_typ=contributions_HH_2030_kerosene_typ.drop(columns=['SAF production'])
contributions_HH_2030_kerosene_typ=contributions_HH_2030_kerosene_typ.reindex([6,4])
#2040
contributions_HH_2040_kerosene_typ=contributions_HH[((contributions_HH['Configuration']=='Conventional')|(contributions_HH['Configuration']=='GT-bat')|(contributions_HH['Configuration']=='PEMFC-bat'))&(contributions_HH['Fuel']=='Kerosene') & (contributions_HH['Mission'] =='typical mission')&(contributions_HH['Year'] =='2040')]
contributions_HH_2040_kerosene_typ=contributions_HH_2040_kerosene_typ.drop(columns=['SAF production'])
contributions_HH_2040_kerosene_typ=contributions_HH_2040_kerosene_typ.reindex([8,5,7])
#2050
contributions_HH_2050_kerosene_typ=contributions_HH[((contributions_HH['Configuration']=='Conventional')|(contributions_HH['Configuration']=='SOFC-bat')|(contributions_HH['Configuration']=='PEMFC-bat'))&(contributions_HH['Fuel']=='Kerosene') & (contributions_HH['Mission'] =='typical mission')&(contributions_HH['Year'] =='2050')]
contributions_HH_2050_kerosene_typ=contributions_HH_2050_kerosene_typ.drop(columns=['SAF production'])
contributions_HH_2050_kerosene_typ=contributions_HH_2050_kerosene_typ.reindex([6,4,5])

# obtaining the contributions of each stage in percent
percentages_HH=contributions_HH
percentages_HH['Combustion %']=percentages_HH['Combustion']*100/percentages_HH['Sum']
percentages_HH['SAF production %']=percentages_HH['SAF production']*100/percentages_HH['Sum']
percentages_HH['Airport use %']=percentages_HH['Airport use']*100/percentages_HH['Sum']
percentages_HH['Kerosene production %']=percentages_HH['Kerosene production']*100/percentages_HH['Sum']
percentages_HH['Electricity production %']=percentages_HH['Electricity production']*100/percentages_HH['Sum']
percentages_HH['Aircraft production %']=percentages_HH['Aircraft production']*100/percentages_HH['Sum']
percentages_HH['Hydrogen production %']=percentages_HH['Hydrogen production']*100/percentages_HH['Sum']
percentages_HH=percentages_HH.drop(columns=['Combustion','SAF production','Airport use','Kerosene production','Electricity production','Aircraft production','Hydrogen production','Sum'])

#2030
percentages_HH_2030_kerosene_typ=percentages_HH[((percentages_HH['Configuration']=='Conventional')|(percentages_HH['Configuration']=='GT-bat'))&(percentages_HH['Fuel']=='Kerosene') & (percentages_HH['Mission'] =='typical mission')&(percentages_HH['Year'] =='2030')]
percentages_HH_2030_kerosene_typ=percentages_HH_2030_kerosene_typ.reindex([6,4])

#2040
percentages_HH_2040_kerosene_typ=percentages_HH[((percentages_HH['Configuration']=='Conventional')|(percentages_HH['Configuration']=='GT-bat')|(percentages_HH['Configuration']=='PEMFC-bat'))&(percentages_HH['Fuel']=='Kerosene') & (percentages_HH['Mission'] =='typical mission')&(percentages_HH['Year'] =='2040')]
percentages_HH_2040_kerosene_typ=percentages_HH_2040_kerosene_typ.reindex([8,5,7])

#2050
percentages_HH_2050_kerosene_typ=percentages_HH[((percentages_HH['Configuration']=='Conventional')|(percentages_HH['Configuration']=='SOFC-bat')|(percentages_HH['Configuration']=='PEMFC-bat'))&(percentages_HH['Fuel']=='Kerosene') & (percentages_HH['Mission'] =='typical mission')&(percentages_HH['Year'] =='2050')]
percentages_HH_2050_kerosene_typ=percentages_HH_2050_kerosene_typ.reindex([6,4,5])

#creating barplot with stacked absolute values
fig, axes = plt.subplots(1, 3, sharex='col', sharey='row',figsize=(13, 7))

contributions_HH_2030_kerosene_typ.drop(columns=['Sum']).plot(kind='bar', x='Configuration', stacked=True, ax=axes[0], legend=False, title='2030', color=palette)
contributions_HH_2040_kerosene_typ.drop(columns=['Sum']).plot(kind='bar', x='Configuration', stacked=True, ax=axes[1], legend=False, title='2040', color=palette)
contributions_HH_2050_kerosene_typ.drop(columns=['Sum']).plot(kind='bar', x='Configuration', stacked=True, ax=axes[2], legend=False, title='2050', color=palette)

axes[0].set_ylabel(r'Human Health [DALY/FU]')
plt.legend(loc='center left', bbox_to_anchor=(0.255, 0.85))
axes[0].text(-0.05, 1.05, 'B', transform=axes[0].transAxes, size=20, weight='bold')


#adding the corresponding percentages onto the graph manually
# Airport Use 2030
for xpos, ypos, yval in zip([0,1],((contributions_HH_2030_kerosene_typ['Airport use'])/2).values, percentages_HH_2030_kerosene_typ['Airport use %'].values):
    axes[0].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11')
# Airport Use 2040
for xpos, ypos, yval in zip([0,1,2],((contributions_HH_2040_kerosene_typ['Airport use'])/2).values, percentages_HH_2040_kerosene_typ['Airport use %'].values):
    axes[1].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11')
# Airport Use 2050
for xpos, ypos, yval in zip([0,1,2],((contributions_HH_2050_kerosene_typ['Airport use'])/2).values, percentages_HH_2050_kerosene_typ['Airport use %'].values):
    axes[2].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11')

#Aircraft production 2030
for xpos, ypos, yval in zip([0,1],(contributions_HH_2030_kerosene_typ['Airport use']+(contributions_HH_2030_kerosene_typ['Aircraft production']/2)).values, percentages_HH_2030_kerosene_typ['Aircraft production %'].values):
    axes[0].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11')
#Aircraft production 2040
for xpos, ypos, yval in zip([0,1,2],(contributions_HH_2040_kerosene_typ['Airport use']+(contributions_HH_2040_kerosene_typ['Aircraft production']/2)).values, percentages_HH_2040_kerosene_typ['Aircraft production %'].values):
    axes[1].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11')  
#Aircraft production 2050
for xpos, ypos, yval in zip([0,1,2],(contributions_HH_2050_kerosene_typ['Airport use']+(contributions_HH_2050_kerosene_typ['Aircraft production']/2)).values, percentages_HH_2050_kerosene_typ['Aircraft production %'].values):
    axes[2].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11')
      
#Combustion 2030
for xpos, ypos, yval in zip([0,1],(contributions_HH_2030_kerosene_typ['Airport use']+contributions_HH_2030_kerosene_typ['Aircraft production']+(contributions_HH_2030_kerosene_typ['Combustion']/2)).values, percentages_HH_2030_kerosene_typ['Combustion %'].values):
    axes[0].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11') 
#Combustion 2040
for xpos, ypos, yval in zip([0,1],(contributions_HH_2040_kerosene_typ['Airport use']+contributions_HH_2040_kerosene_typ['Aircraft production']+(contributions_HH_2040_kerosene_typ['Combustion']/2)).values, percentages_HH_2040_kerosene_typ['Combustion %'].values):
    axes[1].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11')
#Combustion 2050
for xpos, ypos, yval in zip([0],(contributions_HH_2050_kerosene_typ['Airport use']+contributions_HH_2050_kerosene_typ['Aircraft production']+(contributions_HH_2050_kerosene_typ['Combustion']/2)).values, percentages_HH_2050_kerosene_typ['Combustion %'].values):
    axes[2].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11')

#Kerosene production 2030
for xpos, ypos, yval in zip([0,1],(contributions_HH_2030_kerosene_typ['Airport use']+contributions_HH_2030_kerosene_typ['Aircraft production']+contributions_HH_2030_kerosene_typ['Combustion']+(contributions_HH_2030_kerosene_typ['Kerosene production']/2)).values, percentages_HH_2030_kerosene_typ['Kerosene production %'].values):
    axes[0].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11') 
#Kerosene production 2040
for xpos, ypos, yval in zip([0,1],(contributions_HH_2040_kerosene_typ['Airport use']+contributions_HH_2040_kerosene_typ['Aircraft production']+contributions_HH_2040_kerosene_typ['Combustion']+(contributions_HH_2040_kerosene_typ['Kerosene production']/2)).values, percentages_HH_2040_kerosene_typ['Kerosene production %'].values):
    axes[1].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11') 
#Kerosene production 2050
for xpos, ypos, yval in zip([0],(contributions_HH_2050_kerosene_typ['Airport use']+contributions_HH_2050_kerosene_typ['Aircraft production']+contributions_HH_2050_kerosene_typ['Combustion']+(contributions_HH_2050_kerosene_typ['Kerosene production']/2)).values, percentages_HH_2050_kerosene_typ['Kerosene production %'].values):
    axes[2].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11')     
    
#Electricity production 2030
axes[0].text(1, (contributions_HH_2030_kerosene_typ['Airport use']+contributions_HH_2030_kerosene_typ['Aircraft production']+contributions_HH_2030_kerosene_typ['Combustion']+contributions_HH_2030_kerosene_typ['Kerosene production']+(contributions_HH_2030_kerosene_typ['Electricity production']/2)).values[1], f"{percentages_HH_2030_kerosene_typ['Electricity production %'].values[1]:0.1f}%", ha="center", va="center",fontsize='11')
#Electricity production 2040
axes[1].text(1, (contributions_HH_2040_kerosene_typ['Airport use']+contributions_HH_2040_kerosene_typ['Aircraft production']+contributions_HH_2040_kerosene_typ['Combustion']+contributions_HH_2040_kerosene_typ['Kerosene production']+(contributions_HH_2040_kerosene_typ['Electricity production']/2)).values[1], f"{percentages_HH_2040_kerosene_typ['Electricity production %'].values[1]:0.1f}%", ha="center", va="center",fontsize='11')
axes[1].text(2, (contributions_HH_2040_kerosene_typ['Airport use']+contributions_HH_2040_kerosene_typ['Aircraft production']+contributions_HH_2040_kerosene_typ['Combustion']+contributions_HH_2040_kerosene_typ['Kerosene production']+(contributions_HH_2040_kerosene_typ['Electricity production']/2)).values[2], f"{percentages_HH_2040_kerosene_typ['Electricity production %'].values[2]:0.1f}%", ha="center", va="center",fontsize='11')
#Electricity production 2050
axes[2].text(1, (contributions_HH_2050_kerosene_typ['Airport use']+contributions_HH_2050_kerosene_typ['Aircraft production']+contributions_HH_2050_kerosene_typ['Combustion']+contributions_HH_2050_kerosene_typ['Kerosene production']+(contributions_HH_2050_kerosene_typ['Electricity production']/2)).values[1], f"{percentages_HH_2050_kerosene_typ['Electricity production %'].values[1]:0.1f}%", ha="center", va="center",fontsize='11')
axes[2].text(2, (contributions_HH_2050_kerosene_typ['Airport use']+contributions_HH_2050_kerosene_typ['Aircraft production']+contributions_HH_2050_kerosene_typ['Combustion']+contributions_HH_2050_kerosene_typ['Kerosene production']+(contributions_HH_2050_kerosene_typ['Electricity production']/2)).values[2], f"{percentages_HH_2050_kerosene_typ['Electricity production %'].values[2]:0.1f}%", ha="center", va="center",fontsize='11')

#Hydrogen production production 2040
axes[1].text(2,(contributions_HH_2040_kerosene_typ['Airport use']+contributions_HH_2040_kerosene_typ['Aircraft production']+contributions_HH_2040_kerosene_typ['Combustion']+contributions_HH_2040_kerosene_typ['Kerosene production']+contributions_HH_2040_kerosene_typ['Electricity production']+(contributions_HH_2040_kerosene_typ['Hydrogen production']/2)).values[2], f"{percentages_HH_2040_kerosene_typ['Hydrogen production %'].values[2]:0.1f}%", ha="center", va="center",fontsize='11')
#Hydrogen production production 2050
axes[2].text(1,(contributions_HH_2050_kerosene_typ['Airport use']+contributions_HH_2050_kerosene_typ['Aircraft production']+contributions_HH_2050_kerosene_typ['Combustion']+contributions_HH_2050_kerosene_typ['Kerosene production']+contributions_HH_2050_kerosene_typ['Electricity production']+(contributions_HH_2050_kerosene_typ['Hydrogen production']/2)).values[1], f"{percentages_HH_2050_kerosene_typ['Hydrogen production %'].values[1]:0.1f}%", ha="center", va="center",fontsize='11')
#Hydrogen production production 2040
axes[2].text(2,(contributions_HH_2050_kerosene_typ['Airport use']+contributions_HH_2050_kerosene_typ['Aircraft production']+contributions_HH_2050_kerosene_typ['Combustion']+contributions_HH_2050_kerosene_typ['Kerosene production']+contributions_HH_2050_kerosene_typ['Electricity production']+(contributions_HH_2050_kerosene_typ['Hydrogen production']/2)).values[2], f"{percentages_HH_2050_kerosene_typ['Hydrogen production %'].values[2]:0.1f}%", ha="center", va="center",fontsize='11')

axes[0].yaxis.set_major_formatter(lambda x, pos: "{:.0E}".format(x))
plt.legend(bbox_to_anchor=(0.45, -0.25), ncol=3,frameon=False)

plt.savefig("F4B_contribution_stages_deterministic_NDC_HH.svg", format='svg',bbox_inches='tight')

Ecosystem Quality

#merging all life cycle stages dataframes for ecosystem quality damages into one

contributions_EQ=pd.concat([endpoint_stages_contribution_EQ_2030,endpoint_stages_contribution_EQ_2040,endpoint_stages_contribution_EQ_2050])
contributions_EQ=contributions_EQ[['Mission','Configuration','Fuel','Airport use','Aircraft production','SAF production', 'Combustion','Kerosene production','Electricity production','H2','Year']]
contributions_EQ=contributions_EQ.rename({'H2':'Hydrogen production'}, axis='columns')

#summing all damages to obtain the ecosystem quality value
contributions_EQ['Sum']=contributions_EQ.sum(axis = 1, numeric_only=True)

#2030
contributions_EQ_2030_kerosene_typ=contributions_EQ[((contributions_EQ['Configuration']=='Conventional')|(contributions_EQ['Configuration']=='GT-bat'))&(contributions_EQ['Fuel']=='Kerosene') & (contributions_EQ['Mission'] =='typical mission')&(contributions_EQ['Year'] =='2030')]
contributions_EQ_2030_kerosene_typ=contributions_EQ_2030_kerosene_typ.drop(columns=['SAF production'])
contributions_EQ_2030_kerosene_typ=contributions_EQ_2030_kerosene_typ.reindex([6,4])
#2040
contributions_EQ_2040_kerosene_typ=contributions_EQ[((contributions_EQ['Configuration']=='Conventional')|(contributions_EQ['Configuration']=='GT-bat')|(contributions_EQ['Configuration']=='PEMFC-bat'))&(contributions_EQ['Fuel']=='Kerosene') & (contributions_EQ['Mission'] =='typical mission')&(contributions_EQ['Year'] =='2040')]
contributions_EQ_2040_kerosene_typ=contributions_EQ_2040_kerosene_typ.drop(columns=['SAF production'])
contributions_EQ_2040_kerosene_typ=contributions_EQ_2040_kerosene_typ.reindex([8,5,7])
#2050
contributions_EQ_2050_kerosene_typ=contributions_EQ[((contributions_EQ['Configuration']=='Conventional')|(contributions_EQ['Configuration']=='SOFC-bat')|(contributions_EQ['Configuration']=='PEMFC-bat'))&(contributions_EQ['Fuel']=='Kerosene') & (contributions_EQ['Mission'] =='typical mission')&(contributions_EQ['Year'] =='2050')]
contributions_EQ_2050_kerosene_typ=contributions_EQ_2050_kerosene_typ.drop(columns=['SAF production'])
contributions_EQ_2050_kerosene_typ=contributions_EQ_2050_kerosene_typ.reindex([6,4,5])

# obtaining the contributions of each stage in percent
percentages_EQ=contributions_EQ
percentages_EQ['Combustion %']=percentages_EQ['Combustion']*100/percentages_EQ['Sum']
percentages_EQ['SAF production %']=percentages_EQ['SAF production']*100/percentages_EQ['Sum']
percentages_EQ['Airport use %']=percentages_EQ['Airport use']*100/percentages_EQ['Sum']
percentages_EQ['Kerosene production %']=percentages_EQ['Kerosene production']*100/percentages_EQ['Sum']
percentages_EQ['Electricity production %']=percentages_EQ['Electricity production']*100/percentages_EQ['Sum']
percentages_EQ['Aircraft production %']=percentages_EQ['Aircraft production']*100/percentages_EQ['Sum']
percentages_EQ['Hydrogen production %']=percentages_EQ['Hydrogen production']*100/percentages_EQ['Sum']
percentages_EQ=percentages_EQ.drop(columns=['Combustion','SAF production','Airport use','Kerosene production','Electricity production','Aircraft production','Hydrogen production','Sum'])

#2030
percentages_EQ_2030_kerosene_typ=percentages_EQ[((percentages_EQ['Configuration']=='Conventional')|(percentages_EQ['Configuration']=='GT-bat'))&(percentages_EQ['Fuel']=='Kerosene') & (percentages_EQ['Mission'] =='typical mission')&(percentages_EQ['Year'] =='2030')]
percentages_EQ_2030_kerosene_typ=percentages_EQ_2030_kerosene_typ.reindex([6,4])

#2040
percentages_EQ_2040_kerosene_typ=percentages_EQ[((percentages_EQ['Configuration']=='Conventional')|(percentages_EQ['Configuration']=='GT-bat')|(percentages_EQ['Configuration']=='PEMFC-bat'))&(percentages_EQ['Fuel']=='Kerosene') & (percentages_EQ['Mission'] =='typical mission')&(percentages_EQ['Year'] =='2040')]
percentages_EQ_2040_kerosene_typ=percentages_EQ_2040_kerosene_typ.reindex([8,5,7])

#2050
percentages_EQ_2050_kerosene_typ=percentages_EQ[((percentages_EQ['Configuration']=='Conventional')|(percentages_EQ['Configuration']=='SOFC-bat')|(percentages_EQ['Configuration']=='PEMFC-bat'))&(percentages_EQ['Fuel']=='Kerosene') & (percentages_EQ['Mission'] =='typical mission')&(percentages_EQ['Year'] =='2050')]
percentages_EQ_2050_kerosene_typ=percentages_EQ_2050_kerosene_typ.reindex([6,4,5])

#creating barplot with stacked absolute values
fig, axes = plt.subplots(1, 3, sharex='col', sharey='row',figsize=(13, 7))

contributions_EQ_2030_kerosene_typ.drop(columns=['Sum']).plot(kind='bar', x='Configuration', stacked=True, ax=axes[0], legend=False, title='2030', color=palette)
contributions_EQ_2040_kerosene_typ.drop(columns=['Sum']).plot(kind='bar', x='Configuration', stacked=True, ax=axes[1], legend=False, title='2040', color=palette)
contributions_EQ_2050_kerosene_typ.drop(columns=['Sum']).plot(kind='bar', x='Configuration', stacked=True, ax=axes[2], legend=False, title='2050', color=palette)

axes[0].set_ylabel(r'Ecosystem Quality [PDF*m2*year/FU]')
plt.legend(loc='center left', bbox_to_anchor=(0.255, 0.85))
axes[0].text(-0.05, 1.05, 'A', transform=axes[0].transAxes, size=20, weight='bold')


#adding the corresponding percentages onto the graph manually
# Airport Use 2030
for xpos, ypos, yval in zip([0,1],((contributions_EQ_2030_kerosene_typ['Airport use'])/2).values, percentages_EQ_2030_kerosene_typ['Airport use %'].values):
    axes[0].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11')
# Airport Use 2040
for xpos, ypos, yval in zip([0,1,2],((contributions_EQ_2040_kerosene_typ['Airport use'])/2).values, percentages_EQ_2040_kerosene_typ['Airport use %'].values):
    axes[1].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11')
# Airport Use 2050
for xpos, ypos, yval in zip([0,1,2],((contributions_EQ_2050_kerosene_typ['Airport use'])/2).values, percentages_EQ_2050_kerosene_typ['Airport use %'].values):
    axes[2].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11')

#Aircraft production 2030
for xpos, ypos, yval in zip([0,1],(contributions_EQ_2030_kerosene_typ['Airport use']+(contributions_EQ_2030_kerosene_typ['Aircraft production']/2)).values, percentages_EQ_2030_kerosene_typ['Aircraft production %'].values):
    axes[0].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11')
#Aircraft production 2040
for xpos, ypos, yval in zip([0,1,2],(contributions_EQ_2040_kerosene_typ['Airport use']+(contributions_EQ_2040_kerosene_typ['Aircraft production']/2)).values, percentages_EQ_2040_kerosene_typ['Aircraft production %'].values):
    axes[1].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11')  
#Aircraft production 2050
for xpos, ypos, yval in zip([0,1,2],(contributions_EQ_2050_kerosene_typ['Airport use']+(contributions_EQ_2050_kerosene_typ['Aircraft production']/2)).values, percentages_EQ_2050_kerosene_typ['Aircraft production %'].values):
    axes[2].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11')
      
#Combustion 2030
for xpos, ypos, yval in zip([0,1],(contributions_EQ_2030_kerosene_typ['Airport use']+contributions_EQ_2030_kerosene_typ['Aircraft production']+(contributions_EQ_2030_kerosene_typ['Combustion']/2)).values, percentages_EQ_2030_kerosene_typ['Combustion %'].values):
    axes[0].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11') 
#Combustion 2040
for xpos, ypos, yval in zip([0,1],(contributions_EQ_2040_kerosene_typ['Airport use']+contributions_EQ_2040_kerosene_typ['Aircraft production']+(contributions_EQ_2040_kerosene_typ['Combustion']/2)).values, percentages_EQ_2040_kerosene_typ['Combustion %'].values):
    axes[1].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11')
#Combustion 2050
for xpos, ypos, yval in zip([0],(contributions_EQ_2050_kerosene_typ['Airport use']+contributions_EQ_2050_kerosene_typ['Aircraft production']+(contributions_EQ_2050_kerosene_typ['Combustion']/2)).values, percentages_EQ_2050_kerosene_typ['Combustion %'].values):
    axes[2].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11')

#Kerosene production 2030
for xpos, ypos, yval in zip([0,1],(contributions_EQ_2030_kerosene_typ['Airport use']+contributions_EQ_2030_kerosene_typ['Aircraft production']+contributions_EQ_2030_kerosene_typ['Combustion']+(contributions_EQ_2030_kerosene_typ['Kerosene production']/2)).values, percentages_EQ_2030_kerosene_typ['Kerosene production %'].values):
    axes[0].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11') 
#Kerosene production 2040
for xpos, ypos, yval in zip([0,1],(contributions_EQ_2040_kerosene_typ['Airport use']+contributions_EQ_2040_kerosene_typ['Aircraft production']+contributions_EQ_2040_kerosene_typ['Combustion']+(contributions_EQ_2040_kerosene_typ['Kerosene production']/2)).values, percentages_EQ_2040_kerosene_typ['Kerosene production %'].values):
    axes[1].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11') 
#Kerosene production 2050
for xpos, ypos, yval in zip([0],(contributions_EQ_2050_kerosene_typ['Airport use']+contributions_EQ_2050_kerosene_typ['Aircraft production']+contributions_EQ_2050_kerosene_typ['Combustion']+(contributions_EQ_2050_kerosene_typ['Kerosene production']/2)).values, percentages_EQ_2050_kerosene_typ['Kerosene production %'].values):
    axes[2].text(xpos, ypos, f"{yval:0.1f}%", ha="center", va="center",fontsize='11')     
    
#Electricity production 2030
axes[0].text(1, (contributions_EQ_2030_kerosene_typ['Airport use']+contributions_EQ_2030_kerosene_typ['Aircraft production']+contributions_EQ_2030_kerosene_typ['Combustion']+contributions_EQ_2030_kerosene_typ['Kerosene production']+(contributions_EQ_2030_kerosene_typ['Electricity production']/2)).values[1], f"{percentages_EQ_2030_kerosene_typ['Electricity production %'].values[1]:0.1f}%", ha="center", va="center",fontsize='11')
#Electricity production 2040
axes[1].text(1, (contributions_EQ_2040_kerosene_typ['Airport use']+contributions_EQ_2040_kerosene_typ['Aircraft production']+contributions_EQ_2040_kerosene_typ['Combustion']+contributions_EQ_2040_kerosene_typ['Kerosene production']+(contributions_EQ_2040_kerosene_typ['Electricity production']/2)).values[1], f"{percentages_EQ_2040_kerosene_typ['Electricity production %'].values[1]:0.1f}%", ha="center", va="center",fontsize='11')
axes[1].text(2, (contributions_EQ_2040_kerosene_typ['Airport use']+contributions_EQ_2040_kerosene_typ['Aircraft production']+contributions_EQ_2040_kerosene_typ['Combustion']+contributions_EQ_2040_kerosene_typ['Kerosene production']+(contributions_EQ_2040_kerosene_typ['Electricity production']/2)).values[2], f"{percentages_EQ_2040_kerosene_typ['Electricity production %'].values[2]:0.1f}%", ha="center", va="center",fontsize='11')
#Electricity production 2050
axes[2].text(1, (contributions_EQ_2050_kerosene_typ['Airport use']+contributions_EQ_2050_kerosene_typ['Aircraft production']+contributions_EQ_2050_kerosene_typ['Combustion']+contributions_EQ_2050_kerosene_typ['Kerosene production']+(contributions_EQ_2050_kerosene_typ['Electricity production']/2)).values[1], f"{percentages_EQ_2050_kerosene_typ['Electricity production %'].values[1]:0.1f}%", ha="center", va="center",fontsize='11')
axes[2].text(2, (contributions_EQ_2050_kerosene_typ['Airport use']+contributions_EQ_2050_kerosene_typ['Aircraft production']+contributions_EQ_2050_kerosene_typ['Combustion']+contributions_EQ_2050_kerosene_typ['Kerosene production']+(contributions_EQ_2050_kerosene_typ['Electricity production']/2)).values[2], f"{percentages_EQ_2050_kerosene_typ['Electricity production %'].values[2]:0.1f}%", ha="center", va="center",fontsize='11')

#Hydrogen production production 2040
axes[1].text(2,(contributions_EQ_2040_kerosene_typ['Airport use']+contributions_EQ_2040_kerosene_typ['Aircraft production']+contributions_EQ_2040_kerosene_typ['Combustion']+contributions_EQ_2040_kerosene_typ['Kerosene production']+contributions_EQ_2040_kerosene_typ['Electricity production']+(contributions_EQ_2040_kerosene_typ['Hydrogen production']/2)).values[2], f"{percentages_EQ_2040_kerosene_typ['Hydrogen production %'].values[2]:0.1f}%", ha="center", va="center",fontsize='11')
#Hydrogen production production 2050
axes[2].text(1,(contributions_EQ_2050_kerosene_typ['Airport use']+contributions_EQ_2050_kerosene_typ['Aircraft production']+contributions_EQ_2050_kerosene_typ['Combustion']+contributions_EQ_2050_kerosene_typ['Kerosene production']+contributions_EQ_2050_kerosene_typ['Electricity production']+(contributions_EQ_2050_kerosene_typ['Hydrogen production']/2)).values[1], f"{percentages_EQ_2050_kerosene_typ['Hydrogen production %'].values[1]:0.1f}%", ha="center", va="center",fontsize='11')
#Hydrogen production production 2040
axes[2].text(2,(contributions_EQ_2050_kerosene_typ['Airport use']+contributions_EQ_2050_kerosene_typ['Aircraft production']+contributions_EQ_2050_kerosene_typ['Combustion']+contributions_EQ_2050_kerosene_typ['Kerosene production']+contributions_EQ_2050_kerosene_typ['Electricity production']+(contributions_EQ_2050_kerosene_typ['Hydrogen production']/2)).values[2], f"{percentages_EQ_2050_kerosene_typ['Hydrogen production %'].values[2]:0.1f}%", ha="center", va="center",fontsize='11')


plt.legend(bbox_to_anchor=(0.45, -0.25), ncol=3,frameon=False)
plt.savefig("F4A_contribution_stages_deterministic_NDC_EQ.svg", format='svg',bbox_inches='tight')

Figure 5 Comparison of SSP scenarios

only for the typical mission endpoints, without consideration for alternative aviation fuels

endpoints_ST_typical=endpoints_ST[endpoints_ST['Mission']=='Typical']
endpoints_ST_typical_h2_kero=endpoints_ST_typical[(endpoints_ST_typical["Fuel"] != "AAF") & (endpoints_ST_typical["Fuel"] != "ILUC")]

g=sns.catplot(endpoints_ST_typical_h2_kero,x='Configuration',y='Value',row='Damage',col='Year',hue='Scenario',sharey='row',margin_titles=True, sharex=False,kind='box', units='Unit', height=3.5, aspect=1)
sns.move_legend(g, "lower center",bbox_to_anchor=(.5, -0.04), ncol=4,frameon=False)
g.axes[0,0].set_ylabel("Ecosystem Quality [PDF*m2*year/FU]")
g.axes[1,0].set_ylabel("Human Health [DALY/FU]")
g.axes[0,0].set_title("2030")
g.axes[0,1].set_title("2040")
g.axes[0,2].set_title("2050")
g.axes[1,0].set_xlabel('')
g.axes[1,1].set_xlabel('')
g.axes[1,2].set_xlabel('')

#some outliers are excluded due to visibility considerations, the same figure with all outliers is presented in the supporting word file. To obtain that figure with all outliers the next two lines need to be outcommented.
g.axes[0,0].set_ylim(0, 1200)
g.axes[1,0].set_ylim(0, 0.007)
g.axes[1,0].yaxis.set_major_formatter(lambda x, pos: "{:.0E}".format(x))

for i, ax in enumerate(g.axes.flat):
    plt.setp(ax.texts, text='')

plt.savefig("F5_boxplot_endpoint_scenarios_noAAF.svg", format='svg',bbox_inches='tight')
#plt.savefig("SI_F5_boxplot_endpoint_scenarios_noAAF.svg", format='svg',bbox_inches='tight')

Figure 6 Comparison of different fuels

Comparison of kerosene, AAF and AAF+ILUC use as fuels. Only for the endpoint, typical mission and the baseline scenario.

endpoints_NDC=endpoints_ST[endpoints_ST['Scenario']=='NDC']
endpoints_NDC_typical=endpoints_NDC[endpoints_NDC['Mission']=='Typical']
endpoints_NDC_typical=endpoints_NDC_typical.replace(['H2'],'Hydrogen')
endpoints_NDC_typical=endpoints_NDC_typical.replace(['ILUC'],'AAF+ILUC')

g=sns.catplot(endpoints_NDC_typical,x='Configuration',y='Value',row='Damage',col='Year',hue='Fuel',sharey='row',margin_titles=True, sharex=False,kind='box', units='Unit', height=3.5, aspect=1,dodge=True)
sns.move_legend(g, "lower center",bbox_to_anchor=(.5, -0.04), ncol=4,frameon=False)
g.axes[0,0].set_ylabel("Ecosystem Quality [PDF*m2*year/FU]")
g.axes[1,0].set_ylabel("Human Health [DALY/FU]")
g.axes[0,0].set_title("2030")
g.axes[0,1].set_title("2040")
g.axes[0,2].set_title("2050")
g.axes[1,0].set_xlabel('')
g.axes[1,1].set_xlabel('')
g.axes[1,2].set_xlabel('')


#some outliers are excluded due to visibility considerations, the same figure with all outliers is presented in the supporting word file. To obtain that figure with all outliers the next two lines need to be outcommented.
g.axes[0,0].set_ylim(0, 1500)
g.axes[1,0].set_ylim(0, 0.007)
g.axes[1,0].yaxis.set_major_formatter(lambda x, pos: "{:.0E}".format(x))
for i, ax in enumerate(g.axes.flat):
    plt.setp(ax.texts, text='')
    
plt.savefig("F6_boxplot_endpoint_NDC_AAFs.svg", format='svg',bbox_inches='tight')
#plt.savefig("SI_F6_boxplot_endpoint_NDC_AAFs.svg", format='svg',bbox_inches='tight')

Appendix Figure: typical mission vs. design mission

Only for the endpoint baseline scenario with no consideration for alternative aviation fuels

endpoints_NDC=endpoints_ST[endpoints_ST['Scenario']=='NDC']
endpoints_NDC_kero_h2=endpoints_NDC[(endpoints_NDC["Fuel"] != "AAF") & (endpoints_NDC["Fuel"] != "ILUC")]

g=sns.catplot(endpoints_NDC_kero_h2,x='Mission',y='Value',row='Damage',col='Year',hue='Configuration',sharey='row',margin_titles=True, sharex=False,kind='box', units='Unit', height=3.5, aspect=1.1)
sns.move_legend(g, "lower center",bbox_to_anchor=(.5, -0.08), ncol=4,frameon=False)
g.axes[0,0].set_ylabel("Ecosystem Quality   [PDF*m2*year]")
g.axes[1,0].set_ylabel("Human Health   [DALY]")
g.axes[1,0].set_xlabel('')
g.axes[1,1].set_xlabel('')
g.axes[1,2].set_xlabel('')
g.axes[0,0].set_title("2030")
g.axes[0,1].set_title("2040")
g.axes[0,2].set_title("2050")
#zooming in on the y axis - outliers!
g.axes[0,0].set_ylim(0, 1600)
g.axes[1,0].set_ylim(0, 0.015)
g.axes[1,0].yaxis.set_major_formatter(lambda x, pos: "{:.0E}".format(x))
for i, ax in enumerate(g.axes.flat):
    plt.setp(ax.texts, text='')
plt.savefig("SI_boxplot_endpoint_missions_NDC_noAAF.svg", format='svg',bbox_inches='tight')

Appendix figure: comparison of SSP scenarios

Boxplots with midpoint results for the typical mission, with no consideration for alternative aviation fuels

#Midpoint for all ssp scenarios (only kerosene and h2)
MC_results_kero_h2=MC_results_Midpoint[(MC_results_Midpoint["Fuel"] != "AAF") & (MC_results_Midpoint["Fuel"] != "ILUC")]
j=sns.catplot(MC_results_kero_h2,x='Configuration',y='Value',row='Category',col='Year',hue='Scenario',sharey=False, sharex=False,kind='box',margin_titles=True, units='Unit')
sns.move_legend(j, "upper left", bbox_to_anchor=(0.92,1))


for i in range(len(units_IWP_mid)):
    j.axes[i,0].set_ylabel(IWP_mid[i][1]+" ["+units_IWP_mid[i]+"]")
    
j.axes[0,0].set_title("2030")
j.axes[0,1].set_title("2040")
j.axes[0,2].set_title("2050")
for i, ax in enumerate(j.axes.flat):
    plt.setp(ax.texts, text='')
plt.savefig("SI_boxplot_midpoint_scenarios_noAAF.svg", format='svg')

Appendix figure: comparison of fuels

Boxplots with midpoint results for the typical mission with the baseline scenario

#Midpoint for kero, h2, AAF and ILUC in NDC 
MC_results_NDC=MC_results_Midpoint[(MC_results_Midpoint["Scenario"] == "NDC") ]


j=sns.catplot(MC_results_NDC,x='Configuration',y='Value',row='Category',col='Year',hue='Fuel',sharey=False, sharex=False,kind='box',margin_titles=True, units='Unit')
sns.move_legend(j, "upper left", bbox_to_anchor=(0.92,1))

for i in range(len(units_IWP_mid)):
    j.axes[i,0].set_ylabel(IWP_mid[i][1]+" ["+units_IWP_mid[i]+"]")
    

plt.savefig("boxplot_midpoint_NDC_AAFs.svg", format='svg')